Paper Citation Record · LEDGER

An Ensemble Approach for Brain Tumor Segmentation and Synthesis

As of 22 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 4 inbound Pith citation observations for arXiv:2411.17617.

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

pith.paper-citation-record.v1
2411.17617 v1

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T11:58:43.086544Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:09:55.694058Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T17:37:58.211606Z

Reference resolution

32 of 32 outbound references displayed

verified exact1
verified fuzzy21
unresolved10
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2c269632-9143-4864-a700-215dd8b019ac · outbound

This paper cites Survival outcomes and prognostic factors in glioblastoma.

An Ensemble Approach for Brain Tumor Segmentation and Synthesis Survival outcomes and prognostic factors in glioblastoma

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:58:43.615188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9ffe37ac-48d2-4cc8-9307-6303a4feb5b3 · outbound

This paper cites Inter-rater agreement in glioma segmen- tations on longitudinal mri.

An Ensemble Approach for Brain Tumor Segmentation and Synthesis Inter-rater agreement in glioma segmen- tations on longitudinal mri

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:58:43.597796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ff933b5e-e62d-4124-8f08-5d9a2edf65d6 · outbound

This paper cites The 2024 Brain Tumor Segmentation (BraTS) Challenge: Glioma Segmentation on Post-treatment MRI.

An Ensemble Approach for Brain Tumor Segmentation and Synthesis The 2024 Brain Tumor Segmentation (BraTS) Challenge: Glioma Segmentation on Post-treatment MRI

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-12T11:58:42.950489Z

Source-reported events for the cited work

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Observation eaacd3d5-f6b1-4769-98a1-e38e91ac6093 · outbound

This paper cites How we won BraTS 2023 Adult Glioma challenge? Just faking it! Enhanced Synthetic Data Augmentation and Model Ensemble for brain tumour segmentation.

An Ensemble Approach for Brain Tumor Segmentation and Synthesis How we won BraTS 2023 Adult Glioma challenge? Just faking it! Enhanced Synthetic Data Augmentation and Model Ensemble for brain tumour segmentation

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-12T11:58:42.955082Z

Source-reported events for the cited work

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Observation 5dbd0f4e-0c70-447d-899a-bb750345adc4 · outbound

This paper cites 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation.

An Ensemble Approach for Brain Tumor Segmentation and Synthesis 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-12T11:58:42.960096Z

Source-reported events for the cited work

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Observation 3659147b-1e52-408a-aa45-7269b802671a · outbound

This paper cites All-net: Anatomical information lesion-wise loss function integrated into neural network for multiple sclerosis lesion segmentation.

An Ensemble Approach for Brain Tumor Segmentation and Synthesis All-net: Anatomical information lesion-wise loss function integrated into neural network for multiple sclerosis lesion segmentation

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:58:43.581399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation bdd715ba-e6f6-4188-a248-da637ef5b33e · outbound

This paper cites The use of robust local haus- dorff distances in accuracy assessment for image alignment of brain mri.

An Ensemble Approach for Brain Tumor Segmentation and Synthesis The use of robust local haus- dorff distances in accuracy assessment for image alignment of brain mri

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:58:43.563656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6803fa9c-3acd-416d-a295-5d1f8865e6ee · outbound

This paper cites Adversarial inpainting of medical image modalities.

An Ensemble Approach for Brain Tumor Segmentation and Synthesis Adversarial inpainting of medical image modalities

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:58:43.547779Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 62f49344-8757-4b69-8574-518d74caabc6 · outbound

This paper cites Deep learning-based 3d inpainting of brain mr images.

An Ensemble Approach for Brain Tumor Segmentation and Synthesis Deep learning-based 3d inpainting of brain mr images

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:58:43.532566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation abbd25b6-7c1d-4c6e-88d5-68458c20859a · outbound

This paper cites High-resolution mri brain inpainting.

An Ensemble Approach for Brain Tumor Segmentation and Synthesis High-resolution mri brain inpainting

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:58:43.515973Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0de07348-ade5-489e-91b1-29729545ed41 · outbound

This paper cites An Optimization Framework for Processing and Transfer Learning for the Brain Tumor Segmentation.

An Ensemble Approach for Brain Tumor Segmentation and Synthesis An Optimization Framework for Processing and Transfer Learning for the Brain Tumor Segmentation

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-12T11:58:42.988676Z

Source-reported events for the cited work

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Observation ac8c0855-608d-4552-8a33-6b808672806d · outbound

This paper cites Op- timized u-net for brain tumor segmentation.

An Ensemble Approach for Brain Tumor Segmentation and Synthesis Op- timized u-net for brain tumor segmentation

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:58:43.501595Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 579acc77-5168-4b05-836a-f4d4a4775bfd · outbound

This paper cites Kingma and Jimmy Ba.

An Ensemble Approach for Brain Tumor Segmentation and Synthesis Kingma and Jimmy Ba

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-12T11:58:42.998841Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 83364377-e319-4ac4-b3c4-02703f2a48bb · outbound

This paper cites Rhizonet segments plant roots to assess biomass and growth for enabling self- driving labs.

An Ensemble Approach for Brain Tumor Segmentation and Synthesis Rhizonet segments plant roots to assess biomass and growth for enabling self- driving labs

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:58:43.475378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 53901eb7-66cb-4e4e-8220-5c04573b344b · outbound

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

An Ensemble Approach for Brain Tumor Segmentation and Synthesis nnu-net: a self-configuring method for deep learning-based biomedical image segmentation

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-12T11:58:43.008056Z

Source-reported events for the cited work

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Observation b1021b71-496c-41b1-b1bc-bcada30a377a · outbound

This paper cites an unresolved cited work.

An Ensemble Approach for Brain Tumor Segmentation and Synthesis Unresolved cited work

Reference 16

Resolution: unresolved; raw_fallback, observed 2026-08-12T11:58:43.450498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8fb5e0c8-2999-464c-8a9b-8749012dba73 · outbound

This paper cites Transform- ers in medical image segmentation: A review.

An Ensemble Approach for Brain Tumor Segmentation and Synthesis Transform- ers in medical image segmentation: A review

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:58:43.435499Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5f31ef2b-531a-4bff-b559-f4a85b9b5079 · outbound

This paper cites Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images.

An Ensemble Approach for Brain Tumor Segmentation and Synthesis Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:58:43.418061Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5b213002-4765-45bc-8d02-ddf30a4a1feb · outbound

This paper cites U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation.

An Ensemble Approach for Brain Tumor Segmentation and Synthesis U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-12T11:58:43.026561Z

Source-reported events for the cited work

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Observation d4c1f123-94f3-48c6-9d16-c86e5512a278 · outbound

This paper cites Re-diffinet: Modeling discrepancy in tumor segmentation using diffusion models.

An Ensemble Approach for Brain Tumor Segmentation and Synthesis Re-diffinet: Modeling discrepancy in tumor segmentation using diffusion models

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:58:43.398319Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0de66561-ce72-42f9-b5ab-b591e0169cf7 · outbound

This paper cites Domain-adversarial training of neural networks.

An Ensemble Approach for Brain Tumor Segmentation and Synthesis Domain-adversarial training of neural networks

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:58:43.382124Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 37d7e9b3-4ebb-4548-bbbf-a0a24dff7a7b · outbound

This paper cites an unresolved cited work.

An Ensemble Approach for Brain Tumor Segmentation and Synthesis Unresolved cited work

Reference 22

Resolution: unresolved; raw_fallback, observed 2026-08-12T11:58:43.366612Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 43d18e77-ec5b-4d3b-bb44-0bc5f205bc00 · outbound

This paper cites U-mamba: Enhancing long-range dependency for biomedical image segmentation, 2018.

An Ensemble Approach for Brain Tumor Segmentation and Synthesis U-mamba: Enhancing long-range dependency for biomedical image segmentation, 2018

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:58:43.351738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6ccdf997-3c9b-4b82-92e0-d798c992920d · outbound

This paper cites 3D Inception-Based TransMorph: Pre- and Post-operative Multi-contrast MRI Registration in Brain Tumors.

An Ensemble Approach for Brain Tumor Segmentation and Synthesis 3D Inception-Based TransMorph: Pre- and Post-operative Multi-contrast MRI Registration in Brain Tumors

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-08-12T11:58:43.131259Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9b67a029-5794-485f-8501-7dda51484555 · outbound

This paper cites Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde- Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio.

An Ensemble Approach for Brain Tumor Segmentation and Synthesis Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde- Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:58:43.335756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ba2ec41c-2b18-4230-b564-71898882c5f4 · outbound

This paper cites an unresolved cited work.

An Ensemble Approach for Brain Tumor Segmentation and Synthesis Unresolved cited work

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-12T11:58:43.059121Z

Source-reported events for the cited work

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Observation fbb0a4ab-20a7-434d-b919-f5d6c10dcb64 · outbound

This paper cites Wasserstein gan, 2017.

An Ensemble Approach for Brain Tumor Segmentation and Synthesis Wasserstein gan, 2017

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:58:43.308849Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation dbd9ea9a-ad6e-4847-99c9-32cd7d68fe99 · outbound

This paper cites Improved training of wasserstein gans, 2017.

An Ensemble Approach for Brain Tumor Segmentation and Synthesis Improved training of wasserstein gans, 2017

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:58:43.291780Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f4d3da48-c583-41de-8522-92b521732038 · outbound

This paper cites T-former: An efficient transformer for image inpainting.

An Ensemble Approach for Brain Tumor Segmentation and Synthesis T-former: An efficient transformer for image inpainting

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:58:43.276484Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 77cd1be6-5ffd-45f4-acb8-e1086b7f17b1 · outbound

This paper cites Fourier neural operator for parametric partial differential equations, 2021.

An Ensemble Approach for Brain Tumor Segmentation and Synthesis Fourier neural operator for parametric partial differential equations, 2021

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:58:43.260734Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 80804025-273e-48e2-9127-919c8465e711 · outbound

This paper cites Grokfast: Ac- celerated grokking by amplifying slow gradients, 2024.

An Ensemble Approach for Brain Tumor Segmentation and Synthesis Grokfast: Ac- celerated grokking by amplifying slow gradients, 2024

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:58:43.245111Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 824c28c3-204a-41b2-98d0-12a843826c59 · outbound

This paper cites Biomedparse: a biomedical foundation model for image parsing of everything everywhere all at once, 2024.

An Ensemble Approach for Brain Tumor Segmentation and Synthesis Biomedparse: a biomedical foundation model for image parsing of everything everywhere all at once, 2024

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:58:43.228526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Pith citing papers

Observation a64230a8-d0fc-49e5-a6b2-297730d0f3b8 · inbound

Ascribe New Dimensions to Scientific Data Visualization with VR cites this paper.

Ascribe New Dimensions to Scientific Data Visualization with VR An Ensemble Approach for Brain Tumor Segmentation and Synthesis

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-16T12:09:55.694058Z

Source-reported events for the cited work

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Observation 80fee421-0d3d-4b40-a04b-c90fe4247943 · inbound

How We Won the ISLES'24 Challenge by Preprocessing cites this paper.

How We Won the ISLES'24 Challenge by Preprocessing An Ensemble Approach for Brain Tumor Segmentation and Synthesis

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-07T14:33:53.512141Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c6e38ef1-e529-4a57-8360-93e1c9f8aab2 · inbound

GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models cites this paper.

GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models An Ensemble Approach for Brain Tumor Segmentation and Synthesis

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T22:33:54.354420Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 24edfddc-39e1-456e-8037-7ca3cea1ccdd · inbound

Clinically-Informed Preprocessing Improves Stroke Segmentation in Low-Resource Settings cites this paper.

Clinically-Informed Preprocessing Improves Stroke Segmentation in Low-Resource Settings An Ensemble Approach for Brain Tumor Segmentation and Synthesis

Reference 10

Resolution: metadata mismatch; local_arxiv, observed 2026-08-05T17:37:58.285339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.