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Paper Citation Record · LEDGER

CAMEL: A Weakly Supervised Learning Framework for Histopathology Image Segmentation

As of 15 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 0 inbound Pith citation observations for arXiv:1908.10555.

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

pith.paper-citation-record.v1
1908.10555 v1

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T10:45:34.007319Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+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

26 of 26 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ace84813-cfae-4a08-b7ca-4d2386d4bf1d · outbound

This paper cites https://camelyon16.

CAMEL: A Weakly Supervised Learning Framework for Histopathology Image Segmentation https://camelyon16

Reference 1

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Observation 976eb59e-7672-4398-ae90-eba79aad2343 · outbound

This paper cites Tensorflow: A system for large-scale machine learning.

CAMEL: A Weakly Supervised Learning Framework for Histopathology Image Segmentation Tensorflow: A system for large-scale machine learning

Reference 2

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Observation 1a919d6f-2174-4737-8493-2bbb2fed3cee · outbound

This paper cites Weakly super- vised learning of instance segmentation with inter-pixel rela- tions.

CAMEL: A Weakly Supervised Learning Framework for Histopathology Image Segmentation Weakly super- vised learning of instance segmentation with inter-pixel rela- tions

Reference 3

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Observation 0bd1a179-9ab0-4787-96a5-bb63cc04ee03 · outbound

This paper cites Learning pixel-level semantic affinity with image-level supervision for weakly supervised semantic segmentation.

CAMEL: A Weakly Supervised Learning Framework for Histopathology Image Segmentation Learning pixel-level semantic affinity with image-level supervision for weakly supervised semantic segmentation

Reference 4

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Observation 2e8b6135-76b1-4a02-b32e-9b9b6499a427 · outbound

This paper cites Diagnostic assessment of deep learn- ing algorithms for detection of lymph node metastases in women with breast cancer.

CAMEL: A Weakly Supervised Learning Framework for Histopathology Image Segmentation Diagnostic assessment of deep learn- ing algorithms for detection of lymph node metastases in women with breast cancer

Reference 5

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Source-reported events for the cited work

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Observation 3eea638e-9374-491f-ab85-942dfcf4950d · outbound

This paper cites Semantic image seg- mentation with deep convolutional nets and fully connected CRFs.

CAMEL: A Weakly Supervised Learning Framework for Histopathology Image Segmentation Semantic image seg- mentation with deep convolutional nets and fully connected CRFs

Reference 6

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Observation ae87a33c-0104-4889-9508-15cf362ed728 · outbound

This paper cites DeepLab: Semantic im- age segmentation with deep convolutional nets, atrous con- volution, and fully connected CRFs.

CAMEL: A Weakly Supervised Learning Framework for Histopathology Image Segmentation DeepLab: Semantic im- age segmentation with deep convolutional nets, atrous con- volution, and fully connected CRFs

Reference 7

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Source-reported events for the cited work

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Observation 9d3dc2d3-10f4-48b8-8c59-c958787a6df7 · outbound

This paper cites BoxSup: Exploit- ing bounding boxes to supervise convolutional networks for semantic segmentation.

CAMEL: A Weakly Supervised Learning Framework for Histopathology Image Segmentation BoxSup: Exploit- ing bounding boxes to supervise convolutional networks for semantic segmentation

Reference 8

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Source-reported events for the cited work

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Observation 9f2919df-0a63-49af-9232-7ff0b4a4444c · outbound

This paper cites WILDCAT: Weakly supervised learning of deep convnets for image classification, pointwise localiza- tion and segmentation.

CAMEL: A Weakly Supervised Learning Framework for Histopathology Image Segmentation WILDCAT: Weakly supervised learning of deep convnets for image classification, pointwise localiza- tion and segmentation

Reference 9

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Source-reported events for the cited work

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Observation 407e191b-00f1-4a99-81a5-0c124226d9b0 · outbound

This paper cites Multi-evidence filtering and fusion for multi-label classification, object de- tection and semantic segmentation based on weakly super- vised learning.

CAMEL: A Weakly Supervised Learning Framework for Histopathology Image Segmentation Multi-evidence filtering and fusion for multi-label classification, object de- tection and semantic segmentation based on weakly super- vised learning

Reference 10

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Observation 5fc19e4b-2a9f-403f-a150-c10647706834 · outbound

This paper cites Deep residual learning for image recognition.

CAMEL: A Weakly Supervised Learning Framework for Histopathology Image Segmentation Deep residual learning for image recognition

Reference 11

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Source-reported events for the cited work

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Observation b2652189-244d-48db-bcb4-30e4b8531c6f · outbound

This paper cites Weakly-supervised semantic segmentation network with deep seeded region growing.

CAMEL: A Weakly Supervised Learning Framework for Histopathology Image Segmentation Weakly-supervised semantic segmentation network with deep seeded region growing

Reference 12

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Source-reported events for the cited work

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Observation a8ee5cbc-fd23-471f-8960-6a081ca05c86 · outbound

This paper cites Constrained deep weak supervision for histopathology im- age segmentation.

CAMEL: A Weakly Supervised Learning Framework for Histopathology Image Segmentation Constrained deep weak supervision for histopathology im- age segmentation

Reference 13

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Source-reported events for the cited work

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Observation cd54504f-4e81-43ee-b179-fab945f47624 · outbound

This paper cites Simple does it: Weakly supervised instance and semantic segmentation.

CAMEL: A Weakly Supervised Learning Framework for Histopathology Image Segmentation Simple does it: Weakly supervised instance and semantic segmentation

Reference 14

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 92f0a5f9-6ec9-498a-9d46-90f77ca8c752 · outbound

This paper cites Cancer Metastasis Detection With Neural Conditional Random Field.

CAMEL: A Weakly Supervised Learning Framework for Histopathology Image Segmentation Cancer Metastasis Detection With Neural Conditional Random Field

Reference 15

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Source-reported events for the cited work

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Observation 05017229-185f-4940-b391-27bb4fe50761 · outbound

This paper cites ScribbleSup: Scribble-supervised convolutional networks for semantic segmentation.

CAMEL: A Weakly Supervised Learning Framework for Histopathology Image Segmentation ScribbleSup: Scribble-supervised convolutional networks for semantic segmentation

Reference 16

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Observation b4a6b46b-e075-4c0e-990b-f5b2357d9926 · outbound

This paper cites Fast Scannet: Fast and dense analysis of multi-gigapixel whole-slide images for cancer metastasis detection.

CAMEL: A Weakly Supervised Learning Framework for Histopathology Image Segmentation Fast Scannet: Fast and dense analysis of multi-gigapixel whole-slide images for cancer metastasis detection

Reference 17

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Observation fef37813-f29d-402d-bab5-becd1d525a37 · outbound

This paper cites Detecting Cancer Metastases on Gigapixel Pathology Images.

CAMEL: A Weakly Supervised Learning Framework for Histopathology Image Segmentation Detecting Cancer Metastases on Gigapixel Pathology Images

Reference 18

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Observation 678d5978-d671-402a-a237-4fb3be2a210a · outbound

This paper cites Image analysis and ma- chine learning in digital pathology: Challenges and opportu- nities.

CAMEL: A Weakly Supervised Learning Framework for Histopathology Image Segmentation Image analysis and ma- chine learning in digital pathology: Challenges and opportu- nities

Reference 19

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Source-reported events for the cited work

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Observation cc008333-eee2-44ee-83d1-e64134da3572 · outbound

This paper cites U- Net: Convolutional networks for biomedical image segmen- tation.

CAMEL: A Weakly Supervised Learning Framework for Histopathology Image Segmentation U- Net: Convolutional networks for biomedical image segmen- tation

Reference 20

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Observation 4af675ab-9803-46b5-a757-21381649d9d0 · outbound

This paper cites Platt, and Cha Zhang.

CAMEL: A Weakly Supervised Learning Framework for Histopathology Image Segmentation Platt, and Cha Zhang

Reference 21

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Source-reported events for the cited work

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Observation 6500f37f-ceed-408c-8008-fea079dec3a6 · outbound

This paper cites Object region mining with adversarial erasing: A simple classification to semantic segmentation approach.

CAMEL: A Weakly Supervised Learning Framework for Histopathology Image Segmentation Object region mining with adversarial erasing: A simple classification to semantic segmentation approach

Reference 22

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Source-reported events for the cited work

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Observation 003accce-fdd4-4e25-b7db-fca3c2ce86ae · outbound

This paper cites STC: A simple to complex framework for weakly- supervised semantic segmentation.

CAMEL: A Weakly Supervised Learning Framework for Histopathology Image Segmentation STC: A simple to complex framework for weakly- supervised semantic segmentation

Reference 23

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Source-reported events for the cited work

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Observation b1d3f67f-ce9e-43bc-8a44-a057ec741556 · outbound

This paper cites The application of two- level attention models in deep convolutional neural network for fine-grained image classification.

CAMEL: A Weakly Supervised Learning Framework for Histopathology Image Segmentation The application of two- level attention models in deep convolutional neural network for fine-grained image classification

Reference 24

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Source-reported events for the cited work

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Observation 99bc3d3e-3d1b-4821-ba66-813a016907d4 · outbound

This paper cites Deep learning of feature representation with multiple instance learning for medical image analysis.

CAMEL: A Weakly Supervised Learning Framework for Histopathology Image Segmentation Deep learning of feature representation with multiple instance learning for medical image analysis

Reference 25

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Source-reported events for the cited work

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Observation 4764bfd9-a957-41d0-bd55-04deb8c05d3d · outbound

This paper cites Weakly supervised histopathology cancer im- age segmentation and classification.

CAMEL: A Weakly Supervised Learning Framework for Histopathology Image Segmentation Weakly supervised histopathology cancer im- age segmentation and classification

Reference 26

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Source-reported events for the cited work

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Pith citing papers

No inbound Pith citation observations are available.