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Paper Citation Record · LEDGER

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation

As of 10 August 2026, this Paper Citation Record lists 85 of 85 outbound references and 0 inbound Pith citation observations for arXiv:2510.01532.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2510.01532 v2

Coverage vector

measured 85 of 85 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T12:56:16.439916Z

measured 85 of 85 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

85 of 85 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved84
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

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Outbound references

Observation af4c4eb2-31a5-41d9-bb0a-8a5f1e0d2fb0 · outbound

This paper cites Pseudo-label guided contrastive learning for semi-supervised medical image segmentation.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Pseudo-label guided contrastive learning for semi-supervised medical image segmentation

Reference 1

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source=pdf_text observed=2026-08-04T12:56:05.949898Z digest=sha256:4715773df514f125ddfd1ca67b6a8c6a8f016c54521d648c62c352266436c2b8

Observation 17d3b865-41a0-475f-99a9-b4a1bc79e4b4 · outbound

This paper cites Topologically faithful multi-class segmentation in medical images.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Topologically faithful multi-class segmentation in medical images

Reference 2

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source=pdf_text observed=2026-08-04T12:56:06.157154Z digest=sha256:dcfa8061d01dc1d93ee296bcfb804fa230a93dc34c3e6ab6592318510fc24630

Observation c04c1eec-6858-4e1e-ae06-70171e2b5372 · outbound

This paper cites Mixmatch: A holistic approach to semi-supervised learning.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Mixmatch: A holistic approach to semi-supervised learning

Reference 3

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source=pdf_text observed=2026-08-04T12:56:06.323359Z digest=sha256:c06fd28f12b4e95f8c192926db10d9a7edbdfc1a2f6200eae64ea00242bb62b2

Observation a59f4229-5cfd-47f1-b8aa-1045e4856f9e · outbound

This paper cites Encoder-decoder with atrous separable convolution for semantic image segmentation.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Encoder-decoder with atrous separable convolution for semantic image segmentation

Reference 4

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source=pdf_text observed=2026-08-04T12:56:06.436273Z digest=sha256:59660810092d264d13995a0c5f761ed3cedee14830396de662a4390f41bfd8f2

Observation aef9491a-6c44-4bac-b5e1-b23230ef9a01 · outbound

This paper cites A topological loss function for deep-learning based image segmentation using persistent homology.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation A topological loss function for deep-learning based image segmentation using persistent homology

Reference 5

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source=pdf_text observed=2026-08-04T12:56:06.552938Z digest=sha256:3ec7f883c73ab921be8abc4701d1fd4a26d33faabda82c74c4e7d81aff013cf6

Observation d3b5aafe-71fd-479e-912b-a71cf4f2590c · outbound

This paper cites Lipschitz functions have l p-stable persistence.F oundations of Computational Mathematics, 2010.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Lipschitz functions have l p-stable persistence.F oundations of Computational Mathematics, 2010

Reference 6

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source=pdf_text observed=2026-08-04T12:56:06.741059Z digest=sha256:83fc32278630b452fd4e1f2c96262589828f8760d5dfc77500ec01559738447d

Observation 5d3a0361-795c-492b-a24f-42db779e7c69 · outbound

This paper cites American Mathematical Soc., 2010.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation American Mathematical Soc., 2010

Reference 7

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source=pdf_text observed=2026-08-04T12:56:06.776259Z digest=sha256:5dc5b564547290994ddf4c6c0f2df6ff6d5b8eae3751c3332ff681f3a8f5ca2f

Observation e4bf6048-e680-458b-b481-6ca8359a724c · outbound

This paper cites Colorectal carcinoma: Pathologic aspects.Journal of gastrointestinal oncology, 2012.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Colorectal carcinoma: Pathologic aspects.Journal of gastrointestinal oncology, 2012

Reference 8

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source=pdf_text observed=2026-08-04T12:56:06.829694Z digest=sha256:ab89eb9ff4aa694f8cd2c574bf73a2d08b5e5c55d173278ad2713c727a66dc24

Observation 02fd9cec-b7fe-4a21-9255-76a04a2de7d7 · outbound

This paper cites Dropout as a bayesian approximation: Representing model uncertainty in deep learning.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Dropout as a bayesian approximation: Representing model uncertainty in deep learning

Reference 9

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source=pdf_text observed=2026-08-04T12:56:07.064743Z digest=sha256:c59824d954b42d4bb3d99e31e0695b6269a677afda0b542a57350f6cbb07731a

Observation 0eb9626c-c4ce-4bc2-b200-c527619d87f1 · outbound

This paper cites Pmt: Progressive mean teacher via exploring temporal consistency for semi-supervised medical image segmentation.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Pmt: Progressive mean teacher via exploring temporal consistency for semi-supervised medical image segmentation

Reference 10

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source=pdf_text observed=2026-08-04T12:56:07.183916Z digest=sha256:33c4543258828ef90c0f0a749ddb1c68900c7def120f326506e048461d791438

Observation fb962e99-3b37-4538-8d97-f9ea4264e696 · outbound

This paper cites Mild-net: Minimal information loss dilated network for gland instance segmentation in colon histology images.MedIA, 2019.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Mild-net: Minimal information loss dilated network for gland instance segmentation in colon histology images.MedIA, 2019

Reference 11

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source=pdf_text observed=2026-08-04T12:56:07.364746Z digest=sha256:6314314fc28bd26ebdb04a062506ec77905d65283cdbd5753fc50bc8052e559d

Observation b93ec441-9637-44e3-8922-1286fb2952b3 · outbound

This paper cites Hover-net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images.MedIA, 2019.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Hover-net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images.MedIA, 2019

Reference 12

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source=pdf_text observed=2026-08-04T12:56:07.493463Z digest=sha256:fbfd85d4d5068b77db91c3071a999c29b45ef646f6d98b8818afa72415e3e3d3

Observation 017e4ecf-3233-4dcf-8577-807fb21210b5 · outbound

This paper cites Semi-supervised learning by entropy minimization.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Semi-supervised learning by entropy minimization

Reference 13

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source=pdf_text observed=2026-08-04T12:56:07.604808Z digest=sha256:2622ff76024ce0078248a8024cbb74257d7e8f996bb04d880d8d4b6d3cf5aa5f

Observation 0ba7149d-3a2f-403c-911e-c1ae16d8e123 · outbound

This paper cites On calibration of modern neural networks.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation On calibration of modern neural networks

Reference 14

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source=pdf_text observed=2026-08-04T12:56:07.710743Z digest=sha256:dfff6d3c7bd94abc0fd731e1944ace36116ae7b23662503d8d1e3fd25b74b2e3

Observation a5cf952f-8254-4b05-b67c-91d132aaae94 · outbound

This paper cites Learning topological interactions for multi-class medical image segmentation.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Learning topological interactions for multi-class medical image segmentation

Reference 15

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source=pdf_text observed=2026-08-04T12:56:07.788329Z digest=sha256:94fc923bdf0b31f94cc4bc38f5bca691d90282dd7d788267744fb0844f4b5450

Observation a5416a0e-0457-4040-bc86-a2a62eadce22 · outbound

This paper cites Topology-aware uncertainty for image segmentation.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Topology-aware uncertainty for image segmentation

Reference 16

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source=pdf_text observed=2026-08-04T12:56:07.910450Z digest=sha256:439f9067364f9e331c458944e21869810a3b458114a513c733d197456f585480

Observation 0207e6ad-dd13-40ec-9aa3-09de3d0ed57e · outbound

This paper cites Toposeg: Topology-aware nuclear instance segmentation.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Toposeg: Topology-aware nuclear instance segmentation

Reference 17

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source=pdf_text observed=2026-08-04T12:56:07.948205Z digest=sha256:85ec68b2025f3bc9b6df695b4921a3f0a95153defccf9865034bc05872ae2f8e

Observation fbda5ccf-210f-4b49-8f89-43ddaf0f60c0 · outbound

This paper cites Cellvit: Vision transformers for precise cell segmentation and classification.MedIA, 2024.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Cellvit: Vision transformers for precise cell segmentation and classification.MedIA, 2024

Reference 18

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source=pdf_text observed=2026-08-04T12:56:08.064395Z digest=sha256:554e4424e726d52c61a8dd09dd1fcc2cd3cc7ba6e0ae2af99193e5bc4fa3b402

Observation 92e33f1e-8abc-4f01-9d9c-6ee9584b75dd · outbound

This paper cites Lora: Low-rank adaptation of large language models.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Lora: Low-rank adaptation of large language models

Reference 19

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source=pdf_text observed=2026-08-04T12:56:08.170426Z digest=sha256:2fe942ca6f81e9d6b75761f4b6034c9611d197f60cdfde159728f170dea50cc1

Observation d24c2b22-0d86-4242-8e5f-96f10f7fec72 · outbound

This paper cites Structure-aware image segmentation with homotopy warping.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Structure-aware image segmentation with homotopy warping

Reference 20

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source=pdf_text observed=2026-08-04T12:56:08.225667Z digest=sha256:a416f4f4f2e821100fb41df864d0e7bf165b68b7af201f692b68861abb53a76f

Observation 267eae2b-4e8d-4cdb-b4a1-ad7ca2566e6a · outbound

This paper cites Topology-preserving deep image segmentation.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Topology-preserving deep image segmentation

Reference 21

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source=pdf_text observed=2026-08-04T12:56:08.290449Z digest=sha256:c8749b3e13f56faad914d1917645c42dca6da787dd2d1ed4185e34b18b08fb86

Observation c255e075-78bc-4d97-964b-519e9dfbcf91 · outbound

This paper cites Learning probabilistic topological representations using discrete morse theory.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Learning probabilistic topological representations using discrete morse theory

Reference 22

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source=pdf_text observed=2026-08-04T12:56:08.421725Z digest=sha256:7f03566f0c8ead5aa249ca1af2fd416f6b9a88349f5fc173d71a4bbc65ea2b7d

Observation 75ce723c-4533-4a26-981a-cde802fa6dfa · outbound

This paper cites Topology-aware segmentation using discrete morse theory.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Topology-aware segmentation using discrete morse theory

Reference 23

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source=pdf_text observed=2026-08-04T12:56:08.475023Z digest=sha256:36897593080997a2203a983956c8aa88544f22e6f26065da679e8e314626a0cb

Observation 588f930d-db4c-4654-9d08-01ae446cb144 · outbound

This paper cites Adversarial Learning for Semi-Supervised Semantic Segmentation.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Adversarial Learning for Semi-Supervised Semantic Segmentation

Reference 24

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source=pdf_text observed=2026-08-04T12:56:08.652086Z digest=sha256:f27999052eeef88f9f4c296e777fad78229194bdf7b573bc475b3ce2680dab5d

Observation c787525b-18c1-4142-805a-f5960a4b5795 · outbound

This paper cites nnu-net: a self-configuring method for deep learning-based biomedical image segmentation.Nature methods, 2021.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation nnu-net: a self-configuring method for deep learning-based biomedical image segmentation.Nature methods, 2021

Reference 25

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source=pdf_text observed=2026-08-04T12:56:08.765806Z digest=sha256:30cbcbca8803fef4c2f4d672c66c0f4a453f23ab021aa7a34c8375ff53fd125f

Observation f0283de2-7f26-4708-a3bf-cd3080c745bf · outbound

This paper cites An introduction to variational methods for graphical models.Machine learning, 1999.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation An introduction to variational methods for graphical models.Machine learning, 1999

Reference 26

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source=pdf_text observed=2026-08-04T12:56:08.936895Z digest=sha256:7522a1ce8909f70dd6eec334b9e7dd265164f312801ab468d2e61280d9d25e1e

Observation 114b5b10-d119-44bc-9b64-7ddb2d909a94 · outbound

This paper cites Evolutionary characterization of lung adenocarcinoma morphology in tracerx.Nature medicine, 2023.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Evolutionary characterization of lung adenocarcinoma morphology in tracerx.Nature medicine, 2023

Reference 27

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source=pdf_text observed=2026-08-04T12:56:09.084750Z digest=sha256:6443a519b3c854547c31d78b34ca568a6eaaec5d03f93d3812f05a05194eeeee

Observation 20a44285-e5f5-4bd8-8f97-421a842467d1 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Adam: A Method for Stochastic Optimization

Reference 28

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source=pdf_text observed=2026-08-04T12:56:09.228618Z digest=sha256:49141f35bcb0fe2d40ae74a1b9ababa05a6f3c7ce33c72e306437d07a40f465c

Observation 0864547a-0e7f-4b11-b1e0-354680cae3be · outbound

This paper cites Segment anything.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Segment anything

Reference 29

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source=pdf_text observed=2026-08-04T12:56:09.339505Z digest=sha256:1dbbb42f597fe99ec0ad7258bd30a5f060da88ca15b99b2a18a16eb24cc0db25

Observation 3636d59d-eef3-4da8-a669-9807e05bddb0 · outbound

This paper cites Enhancing sam with efficient prompting and preference optimization for semi-supervised medical image segmentation.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Enhancing sam with efficient prompting and preference optimization for semi-supervised medical image segmentation

Reference 30

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source=pdf_text observed=2026-08-04T12:56:09.494684Z digest=sha256:73421a4cb2de33f6f1e6d19cdc5a0fbb3de155d387fa4666fc2c7ed551299d1a

Observation dab5975e-442f-498d-a20f-b54931e91ea3 · outbound

This paper cites The hungarian method for the assignment problem.Naval research logistics quarterly, 1955.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation The hungarian method for the assignment problem.Naval research logistics quarterly, 1955

Reference 31

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source=pdf_text observed=2026-08-04T12:56:09.632894Z digest=sha256:e0d3931fdfd271eec64b1beec671b4967d8e2243972e7206c748ec45d47d15c0

Observation 87e45d33-ea16-44ce-897d-cbf3b761a55e · outbound

This paper cites A multi-organ nucleus segmentation challenge.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation A multi-organ nucleus segmentation challenge

Reference 32

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source=pdf_text observed=2026-08-04T12:56:09.744736Z digest=sha256:b98bb0d5eca764a32fbdc98c8c3b33b2505c3dbcb927d8bcc463b6f50235ee53

Observation 5f2c5359-156f-4c68-933e-94d58ffd4436 · outbound

This paper cites Temporal Ensembling for Semi-Supervised Learning.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Temporal Ensembling for Semi-Supervised Learning

Reference 33

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source=pdf_text observed=2026-08-04T12:56:09.841201Z digest=sha256:c968ce7de210fc52a3afc03b69b6ead3bb4879bed72a18b93a24950ef3b2b410

Observation a3b1ad85-5037-4fb7-9239-3c15d6342440 · outbound

This paper cites Semi-supervised medical image segmentation using adversarial consistency learning and dynamic convolution network.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Semi-supervised medical image segmentation using adversarial consistency learning and dynamic convolution network

Reference 34

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source=pdf_text observed=2026-08-04T12:56:10.037139Z digest=sha256:b66dacad9186cc999e9db353b26ac34fcb5ec3466787242f675a4ab9df1cbacf

Observation ab4c5bae-c7a1-4ce2-a0ab-d91db7b11d3b · outbound

This paper cites Calibrating uncertainty for semi-supervised crowd counting.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Calibrating uncertainty for semi-supervised crowd counting

Reference 35

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source=pdf_text observed=2026-08-04T12:56:10.132878Z digest=sha256:9d9da03a7c28d92eea95b0603a34a8a024a772a06b623bc3a458f0f9d2235207

Observation 384c9ac3-dcd3-4193-b8c4-3caf7c80f59d · outbound

This paper cites Confidence estimation using unlabeled data.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Confidence estimation using unlabeled data

Reference 36

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source=pdf_text observed=2026-08-04T12:56:10.187856Z digest=sha256:a078efe04fe3e0d410cadf80e639e3f9396cee93c26d51a9ebeeb2768407a4c2

Observation 61c00a3b-c605-456f-b58f-5d32fa5b5cbb · outbound

This paper cites Transformation- consistent self-ensembling model for semi-supervised medical image segmentation.TNNLS, 2020.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Transformation- consistent self-ensembling model for semi-supervised medical image segmentation.TNNLS, 2020

Reference 37

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source=pdf_text observed=2026-08-04T12:56:10.279871Z digest=sha256:f958f4f698757f1abfb8a3c23e05fda8396a6e3244405e6ab29b159d3d024a67

Observation b745f622-49e1-48df-8e1d-c528a3ead88b · outbound

This paper cites Semi-supervised medical image segmentation through dual-task consistency.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Semi-supervised medical image segmentation through dual-task consistency

Reference 38

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source=pdf_text observed=2026-08-04T12:56:10.466438Z digest=sha256:709210247fee9b364a3e664e35b919bac7247845a745b71eeae0ed063b28a176

Observation 9b672404-ad97-47a9-bde8-f5b91b29a773 · outbound

This paper cites Semi-supervised medical image segmentation via uncertainty rectified pyramid consistency.MedIA, 2022.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Semi-supervised medical image segmentation via uncertainty rectified pyramid consistency.MedIA, 2022

Reference 39

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source=pdf_text observed=2026-08-04T12:56:10.531951Z digest=sha256:1b002b6879e6e1797aed6e7c6b63cf62545d8e6d16d536b2110b54cc0c468069

Observation 6e9da841-22cb-45c3-a6a1-37855beeae3d · outbound

This paper cites Topograph: An efficient graph-based framework for strictly topology preserving image segmentation.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Topograph: An efficient graph-based framework for strictly topology preserving image segmentation

Reference 40

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source=pdf_text observed=2026-08-04T12:56:10.554338Z digest=sha256:f577b0a6631324ef7302a332e39288a5bb2a9270d15bdd100d98eb0b7240aed9

Observation a540d443-1587-4147-878b-4b47ab7cd76e · outbound

This paper cites Segment anything in medical images.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Segment anything in medical images

Reference 41

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source=pdf_text observed=2026-08-04T12:56:10.584235Z digest=sha256:e93fb9dde77667ea3b5ce897ffb8ea0b6d7c5a5be26b454575f59c6855d8d00a

Observation 3f901993-a609-4630-bf01-15edacb9ce99 · outbound

This paper cites University of Toronto (Canada), 2013.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation University of Toronto (Canada), 2013

Reference 42

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source=pdf_text observed=2026-08-04T12:56:10.639478Z digest=sha256:1c89e52ad40bb70a51a5cef45b049e2c5f68a2a784c347ff6be25901f0ad0e5f

Observation 261e57ae-d76f-430f-86cb-cfed83a89487 · outbound

This paper cites an unresolved cited work.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Unresolved cited work

Reference 43

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source=pdf_text observed=2026-08-04T12:56:10.743348Z digest=sha256:f08bf33031154eecbbe875f680c3848b86c99676122b2b225bebd1fd9377bf9a

Observation 94825a81-16bb-48b5-b7fa-c9e9c6e9ed9f · outbound

This paper cites Exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation.MedIA, 2020.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation.MedIA, 2020

Reference 44

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source=pdf_text observed=2026-08-04T12:56:10.916491Z digest=sha256:d0f9c5720d6adedf49c52e1cdd0a5771c7e636c83c880caf6ba0547c97a29dbc

Observation f3ee9544-477f-43c9-a5cf-bd9281fc3c2f · outbound

This paper cites Semi-supervised histopathology image segmentation with feature diversified collaborative learning.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Semi-supervised histopathology image segmentation with feature diversified collaborative learning

Reference 45

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source=pdf_text observed=2026-08-04T12:56:10.997382Z digest=sha256:c1ae726a6b23c3500a737b980a2c085b8b9a3c2f4cda2dd1b273192dff6331f2

Observation 4f0b5dfd-bf58-4e0b-a4aa-8852c556fa87 · outbound

This paper cites Semi-supervised semantic segmentation with cross- consistency training.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Semi-supervised semantic segmentation with cross- consistency training

Reference 46

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source=pdf_text observed=2026-08-04T12:56:11.087948Z digest=sha256:e6c2857828b8ec8d09686b70f00cb6ffa09b34c2a4fda83323f9ab1e8d5c4946

Observation 5e62b2dd-026b-4709-a120-0c9ceb18ff61 · outbound

This paper cites PyTorch: An Imperative Style, High-Performance Deep Learning Library.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation PyTorch: An Imperative Style, High-Performance Deep Learning Library

Reference 47

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source=pdf_text observed=2026-08-04T12:56:11.148436Z digest=sha256:108190e2c6a0fe995236f491830aaee2534ff1f37f78c406d0e85ee263a4c3db

Observation 6bb42bc6-e0fd-496b-b813-09abc8870ca5 · outbound

This paper cites an unresolved cited work.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Unresolved cited work

Reference 48

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source=pdf_text observed=2026-08-04T12:56:11.207743Z digest=sha256:987733e5e9b9c42340eb2d5225342051fd54f11726c1fae4a4b536ed82b108b9

Observation 2f14b07d-ce8d-4794-a455-f93565c1e38b · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation U-net: Convolutional networks for biomedical image segmentation

Reference 49

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source=pdf_text observed=2026-08-04T12:56:11.304404Z digest=sha256:fed3a45ef3b5dcdeb78f1380b3b57a57d250040406556bc2cdec87605c37c459

Observation 770a3510-2abe-46eb-8a08-b7334c51cbf4 · outbound

This paper cites Reference-guided pseudo- label generation for medical semantic segmentation.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Reference-guided pseudo- label generation for medical semantic segmentation

Reference 50

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source=pdf_text observed=2026-08-04T12:56:11.491359Z digest=sha256:76a97b48233d1d52676ae16e469e4bba6e14f4aee5895fc7d2eceaee71296232

Observation db5af80f-e3c4-450e-8dda-bb274c6524c9 · outbound

This paper cites Revisiting and maximizing temporal knowledge in semi-supervised semantic segmentation.arXiv preprint arXiv:2405.20610, 2024.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Revisiting and maximizing temporal knowledge in semi-supervised semantic segmentation.arXiv preprint arXiv:2405.20610, 2024

Reference 51

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source=pdf_text observed=2026-08-04T12:56:11.638859Z digest=sha256:439aeeedd02233f5126aa801dd50ca02a8258d42f9654890a4d0d3926b1d6fc0

Observation 1aa6ee1f-175e-4d80-a5ac-d333eebb9114 · outbound

This paper cites cldice-a novel topology-preserving loss function for tubular structure segmentation.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation cldice-a novel topology-preserving loss function for tubular structure segmentation

Reference 52

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source=pdf_text observed=2026-08-04T12:56:11.764791Z digest=sha256:68a886eafcb296092073be65f75146142a3356f403a1c0b89b493a8739c0b4d1

Observation 3d22fe43-ced0-4038-8284-30fd3b250fe5 · outbound

This paper cites Gland segmentation in colon histology images: The glas challenge contest.MedIA, 2017.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Gland segmentation in colon histology images: The glas challenge contest.MedIA, 2017

Reference 53

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source=pdf_text observed=2026-08-04T12:56:11.863234Z digest=sha256:9b931b1bcca2e7717323a9df23db4f34a6008367466770050de17bcac2eff66a

Observation 9fc0d189-b1d5-4a22-a581-d055d4565143 · outbound

This paper cites Tint fill.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Tint fill

Reference 54

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source=pdf_text observed=2026-08-04T12:56:11.973586Z digest=sha256:0f00376864d0b4d6858c45276ee954ba157505d03781ee269a484b35760d2503

Observation 6d4f8dec-13fd-4762-92f5-8f04f92a3fbf · outbound

This paper cites Fixmatch: Simplifying semi-supervised learning with consistency and confidence.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Fixmatch: Simplifying semi-supervised learning with consistency and confidence

Reference 55

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source=pdf_text observed=2026-08-04T12:56:12.135902Z digest=sha256:dd28472739567f18214bd867601a94932ab8723e747cea03f3816224e62a4faf

Observation 165f75e2-1602-40d7-b850-6bed605992c7 · outbound

This paper cites Topologically faithful image segmentation via induced matching of persistence barcodes.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Topologically faithful image segmentation via induced matching of persistence barcodes

Reference 56

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source=pdf_text observed=2026-08-04T12:56:12.332769Z digest=sha256:01882627290cb335c97be24c303e9a9b9e08e2b5ea1c79bca0c286cf70217aab

Observation bce9f418-13d0-4901-b719-6ee4cd06997f · outbound

This paper cites Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results

Reference 57

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source=pdf_text observed=2026-08-04T12:56:12.458186Z digest=sha256:9684661d7813b2f5f9e3963d939497d1bb9163e0472a5ec166296440076bba2c

Observation ff9d8c31-5691-46f7-86b4-ab27fb422963 · outbound

This paper cites Monusac2020: A multi-organ nuclei segmentation and classification challenge.TMI, 2021.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Monusac2020: A multi-organ nuclei segmentation and classification challenge.TMI, 2021

Reference 58

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source=pdf_text observed=2026-08-04T12:56:12.599212Z digest=sha256:b3d47484ca5e04dde3b589c3416faaf95c664986f76a490f720705279f5fcaa9

Observation 0ac48c2c-eb20-49d9-ae91-7f4cb4bb75f5 · outbound

This paper cites Advent: Adversarial entropy minimization for domain adaptation in semantic segmentation.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Advent: Adversarial entropy minimization for domain adaptation in semantic segmentation

Reference 59

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source=pdf_text observed=2026-08-04T12:56:12.783727Z digest=sha256:c1f65a381b29dfea6ad3f011e76e362fa2c92f2744feab1612605dc8f8ef8a1b

Observation 6cbc8899-f5c7-47dd-b709-04236403fc53 · outbound

This paper cites Topogan: A topology-aware generative adversarial network.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Topogan: A topology-aware generative adversarial network

Reference 60

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source=pdf_text observed=2026-08-04T12:56:12.892212Z digest=sha256:84fa17a88af06f80e9c6b1443dbe8d8cd44d23ba30e417ffc7d899bca27b1684

Observation 3ea7d9ff-e21d-4381-84dc-3e14e22ba374 · outbound

This paper cites Ta-net: Topology-aware network for gland segmenta- tion.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Ta-net: Topology-aware network for gland segmenta- tion

Reference 61

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source=pdf_text observed=2026-08-04T12:56:13.062470Z digest=sha256:c42c9c028b37b46a6961ab8c376e3e035c1c8938d287c6f048521c379b08e65e

Observation 46fb5f7f-9c28-4640-a9a8-50e869a82bde · outbound

This paper cites Semi-supervised medical image segmentation via a tripled-uncertainty guided mean teacher model with contrastive learning.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Semi-supervised medical image segmentation via a tripled-uncertainty guided mean teacher model with contrastive learning

Reference 62

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source=pdf_text observed=2026-08-04T12:56:13.236286Z digest=sha256:1108a4f5ed1bf43f0263a541284e79cc71b2a60eff965152ed621a7bc9165ae7

Observation 646e7a2a-fffd-414d-a3b7-3b050e351861 · outbound

This paper cites Topology-preserving image segmentation with spatial-aware persistent feature matching.arXiv preprint arXiv:2412.02076, 2024.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Topology-preserving image segmentation with spatial-aware persistent feature matching.arXiv preprint arXiv:2412.02076, 2024

Reference 63

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source=pdf_text observed=2026-08-04T12:56:13.400333Z digest=sha256:76b6818cdb4ae344a0cb82b765cc9e4ed608b8dbdcc81193e86b74bc267fdf3b

Observation 0854df8f-2be9-49ca-a6f5-2af77b407503 · outbound

This paper cites Cross-patch dense contrastive learning for semi-supervised segmentation of cellular nuclei in histopathologic images.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Cross-patch dense contrastive learning for semi-supervised segmentation of cellular nuclei in histopathologic images

Reference 64

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source=pdf_text observed=2026-08-04T12:56:13.587159Z digest=sha256:04366d7e35070a37428628a5807ccd8cc605726b194a2331ec0d0b6c3b89cf96

Observation 77e837d5-40f0-491d-ac02-fd2faf8c283c · outbound

This paper cites Otoo, and Kenji Suzuki.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Otoo, and Kenji Suzuki

Reference 65

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source=pdf_text observed=2026-08-04T12:56:13.681027Z digest=sha256:002b8d5bd51791988667d82718455eadeb2cded0f12543b76ec63f0cdad55e56

Observation e332004f-41f1-4b30-aa76-c655d08364a4 · outbound

This paper cites Entropy-guided contrastive learning for semi-supervised medical image segmentation.IET Image Processing, 2024.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Entropy-guided contrastive learning for semi-supervised medical image segmentation.IET Image Processing, 2024

Reference 66

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source=pdf_text observed=2026-08-04T12:56:13.793942Z digest=sha256:191f87ede6e7910c7a742d69673adaec5a1af2a242702954138d2e727a32856d

Observation 83890439-5aff-4fd4-8abf-aa5174b3f9ad · outbound

This paper cites Deep segmentation-emendation model for gland instance segmentation.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Deep segmentation-emendation model for gland instance segmentation

Reference 67

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source=pdf_text observed=2026-08-04T12:56:13.935270Z digest=sha256:81dd47ea8a3af306cff35a1e744118e65a1cfcf95e2453eee122449019aec9f6

Observation 1cd49c59-5dda-4a7d-9b33-1ba08748135b · outbound

This paper cites Topocellgen: Generating histopathology cell topology with a diffusion model.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Topocellgen: Generating histopathology cell topology with a diffusion model

Reference 68

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source=pdf_text observed=2026-08-04T12:56:14.087878Z digest=sha256:f0f36565c2f66695a7df03d24143a10363b089093cfad983fe20b1054fdcb511

Observation 035096f9-4d3c-4765-a180-4a95c2984ee8 · outbound

This paper cites Semi-supervised segmentation of histopathology images with noise-aware topological consistency.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Semi-supervised segmentation of histopathology images with noise-aware topological consistency

Reference 69

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source=pdf_text observed=2026-08-04T12:56:14.219287Z digest=sha256:bb1abf0909af95ee4da5343e61a2ecb763f8cfa0c5a4f616a50bd99e33cf7e57

Observation e374268f-0e6a-49f8-9655-422eecf793d1 · outbound

This paper cites Ambiguity-selective consistency regularization for mean-teacher semi-supervised medical image segmentation.MedIA, 2023.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Ambiguity-selective consistency regularization for mean-teacher semi-supervised medical image segmentation.MedIA, 2023

Reference 70

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source=pdf_text observed=2026-08-04T12:56:14.383042Z digest=sha256:6277d699afbfa242b8c5e41537e653f9cc53b9d22f61e1d94a22a78f973ba5bf

Observation 38e3e782-77b3-44a9-9355-3eb2ce4a4f26 · outbound

This paper cites 3d topology-preserving segmentation with compound multi-slice representation.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation 3d topology-preserving segmentation with compound multi-slice representation

Reference 71

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source=pdf_text observed=2026-08-04T12:56:14.556027Z digest=sha256:79a984a78fa71273e16cb7d7a68ecb4634885219c2066413b479133ffdc6dc10

Observation 65486d12-df69-48e9-af73-1ae5a9924837 · outbound

This paper cites A topological-attention convlstm network and its application to em images.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation A topological-attention convlstm network and its application to em images

Reference 72

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source=pdf_text observed=2026-08-04T12:56:14.722831Z digest=sha256:dacab84722e39ed13d293484a5715a73c1a8e39b8ba5fcfed09941909e1704e7

Observation 426cefa7-98c1-4079-995b-aead486dd839 · outbound

This paper cites Anomaly-guided weakly supervised lesion segmentation on retinal oct images.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Anomaly-guided weakly supervised lesion segmentation on retinal oct images

Reference 73

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source=pdf_text observed=2026-08-04T12:56:14.843117Z digest=sha256:3f0e4193273aca5f3f5032c2ad719ceac32870ef56eda9befa5cfe8e54eeb3ee

Observation 13015e7d-468c-48cb-8cbb-cab397b6a8b0 · outbound

This paper cites A multimodal approach combining structural and cross-domain textual guidance for weakly supervised oct segmentation.arXiv preprint arXiv:2411.12615, 2024.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation A multimodal approach combining structural and cross-domain textual guidance for weakly supervised oct segmentation.arXiv preprint arXiv:2411.12615, 2024

Reference 74

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source=pdf_text observed=2026-08-04T12:56:14.957949Z digest=sha256:7567061155fb525df1143f5dd77179cce097b12e36152ed8f308b0929ca80d09

Observation bf847855-60c4-4aba-bee0-c959e7752ea4 · outbound

This paper cites Enhancing pseudo label quality for semi-supervised domain-generalized medical image segmentation.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Enhancing pseudo label quality for semi-supervised domain-generalized medical image segmentation

Reference 75

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no resolver link, observed 2026-08-04T12:56:15.074782Z

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source=pdf_text observed=2026-08-04T12:56:15.074782Z digest=sha256:e2608178b725ad78e31fd78cd303126d5a1f9f193c8f13373b723240ddb4f0b4

Observation a9b283c1-6813-4b86-ad6d-4e325fd43f39 · outbound

This paper cites Mine your own anatomy: Revisiting medical image segmentation with extremely limited labels.TPAMI, 2024.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Mine your own anatomy: Revisiting medical image segmentation with extremely limited labels.TPAMI, 2024

Reference 76

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no resolver link, observed 2026-08-04T12:56:15.265804Z

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source=pdf_text observed=2026-08-04T12:56:15.265804Z digest=sha256:4f5453712e663421815589a1629e5bd6380ed47387df7f5125c0428ef1587cde

Observation 10023792-9389-478d-bf02-e49e1a108c58 · outbound

This paper cites Rethinking semi-supervised medical image segmentation: A variance-reduction perspective.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Rethinking semi-supervised medical image segmentation: A variance-reduction perspective

Reference 77

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no resolver link, observed 2026-08-04T12:56:15.378080Z

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source=pdf_text observed=2026-08-04T12:56:15.378080Z digest=sha256:fcaab9136b82fb8ec7bd40c34da86fb9ebbd3891e9a441f2f0192199dc082f2f

Observation b7b6c423-e883-43e2-b560-aeee2d394b41 · outbound

This paper cites Simcvd: Simple contrastive voxel-wise representation distillation for semi-supervised medical image segmentation.TMI, 2022.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Simcvd: Simple contrastive voxel-wise representation distillation for semi-supervised medical image segmentation.TMI, 2022

Reference 78

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no resolver link, observed 2026-08-04T12:56:15.491694Z

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source=pdf_text observed=2026-08-04T12:56:15.491694Z digest=sha256:5702fb1cbeec590e9ba4c960213cd47f3aee2b057a157605b33b5c16dc2ab4dd

Observation be916d6a-29c3-437e-a3a6-ef402ff70a6e · outbound

This paper cites Uncertainty-aware self- ensembling model for semi-supervised 3d left atrium segmentation.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Uncertainty-aware self- ensembling model for semi-supervised 3d left atrium segmentation

Reference 79

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no resolver link, observed 2026-08-04T12:56:15.626591Z

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source=pdf_text observed=2026-08-04T12:56:15.626591Z digest=sha256:67520b22c33980df3a2c273bf2c09fad992be526c610ec8ebd06b06b52057a7b

Observation ccdc36cf-dd85-49cd-82de-a330482f1b48 · outbound

This paper cites Topology-preserving hard pixel mining for tubular structure segmentation.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Topology-preserving hard pixel mining for tubular structure segmentation

Reference 80

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no resolver link, observed 2026-08-04T12:56:15.776738Z

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source=pdf_text observed=2026-08-04T12:56:15.776738Z digest=sha256:53f4ff085b9a0cb68e96709a0f57b8584b0c632c44193ecc93469156a156b7a0

Observation 90dd270b-8dc7-49c5-94d0-8c9274f6ee7c · outbound

This paper cites Boostmis: Boosting medical image semi-supervised learning with adaptive pseudo labeling and informative active annotation.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Boostmis: Boosting medical image semi-supervised learning with adaptive pseudo labeling and informative active annotation

Reference 81

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no resolver link, observed 2026-08-04T12:56:15.919595Z

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source=pdf_text observed=2026-08-04T12:56:15.919595Z digest=sha256:51adfaf8c742e1e8c87262506f14e0d16fa2eb2378be1b3125ae88c815626471

Observation 627a22da-19e5-4811-92f9-8a1a75adcf05 · outbound

This paper cites Discriminative error prediction network for semi-supervised colon gland segmentation.MedIA, 2022.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Discriminative error prediction network for semi-supervised colon gland segmentation.MedIA, 2022

Reference 82

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no resolver link, observed 2026-08-04T12:56:16.099296Z

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source=pdf_text observed=2026-08-04T12:56:16.099296Z digest=sha256:5b625ff5af12283725faeac92c4040fffed19a87c57009c44a1ee1079bbdb99e

Observation ae055a9a-c8a1-49c0-8d8f-e854e8b14c5d · outbound

This paper cites Xnet: Wavelet-based low and high frequency fusion networks for fully-and semi-supervised semantic segmentation of biomedical images.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Xnet: Wavelet-based low and high frequency fusion networks for fully-and semi-supervised semantic segmentation of biomedical images

Reference 83

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no resolver link, observed 2026-08-04T12:56:16.203987Z

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source=pdf_text observed=2026-08-04T12:56:16.203987Z digest=sha256:6dfdd01e348b08f8152b5149ce8f429fda488f5f56220abdce173617525dc933

Observation 8dfacda8-220b-4bfd-a4a6-b0ee52f4a41e · outbound

This paper cites Xnet v2: Fewer limitations, better results and greater universality.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Xnet v2: Fewer limitations, better results and greater universality

Reference 84

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no resolver link, observed 2026-08-04T12:56:16.347010Z

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source=pdf_text observed=2026-08-04T12:56:16.347010Z digest=sha256:4b3e4e2b7b53cfe51c0d31db85eb374b1651ebf13a89c83d48c39667a2e1ba4c

Observation f15d4e67-2072-41ce-af40-bc7f2b093335 · outbound

This paper cites Unet++: A nested u-net architecture for medical image segmentation.

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation Unet++: A nested u-net architecture for medical image segmentation

Reference 85

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malformed identifier
no resolver link, observed 2026-08-04T12:56:16.439916Z

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source=pdf_text observed=2026-08-04T12:56:16.439916Z digest=sha256:33c765463601c85801ec8fd84f48b05bd46b63fb8fe3763d34b23799683916ac

Pith citing papers

No inbound Pith citation observations are available.