Paper Citation Record · LEDGER

Abnormality-Driven Representation Learning for Radiology Imaging

As of 21 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2411.16803.

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

pith.paper-citation-record.v1
2411.16803 v1

Coverage vector

measured 34 of 34 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-12T13:15:06.267935Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

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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

34 of 34 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 253e7b29-7d7c-411f-91b7-ab7c1d46425c · outbound

This paper cites Decoding tumour phenotype by noninvasive imaging using a quantitative ra- diomics approach.

Abnormality-Driven Representation Learning for Radiology Imaging Decoding tumour phenotype by noninvasive imaging using a quantitative ra- diomics approach

Reference 1

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Observation 0228cd15-4f00-4b12-a626-b2b454237a1f · outbound

This paper cites an unresolved cited work.

Abnormality-Driven Representation Learning for Radiology Imaging Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-12T13:15:06.748920Z

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Observation c3109d0d-5d04-461d-87b2-2e5af59c9099 · outbound

This paper cites Joint transformer ar- chitecture in brain 3d mri classification: its application in alzheimer’s disease classification.

Abnormality-Driven Representation Learning for Radiology Imaging Joint transformer ar- chitecture in brain 3d mri classification: its application in alzheimer’s disease classification

Reference 3

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Observation cc226246-587b-44fa-9d85-640fb31ec5e7 · outbound

This paper cites an unresolved cited work.

Abnormality-Driven Representation Learning for Radiology Imaging Unresolved cited work

Reference 4

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Observation 448c76cc-8428-4a58-b73d-5cf651e3368e · outbound

This paper cites Predicting cancer out- comes with radiomics and artificial intelligence in radiology.

Abnormality-Driven Representation Learning for Radiology Imaging Predicting cancer out- comes with radiomics and artificial intelligence in radiology

Reference 5

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Observation b21617c8-2922-4ddc-b440-2c90892b4910 · outbound

This paper cites Shah, An- drew Johnston, Robert D.

Abnormality-Driven Representation Learning for Radiology Imaging Shah, An- drew Johnston, Robert D

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:15:06.694574Z

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Observation 32f9b47b-2d35-4a0d-9f33-b3ad234f94d9 · outbound

This paper cites Towards a general-purpose foundation model for computational pathol- ogy.

Abnormality-Driven Representation Learning for Radiology Imaging Towards a general-purpose foundation model for computational pathol- ogy

Reference 7

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Observation 07d74f88-0adb-44ad-b959-29d976fa1126 · outbound

This paper cites An Empirical Study of Training Self-Supervised Vision Transformers.

Abnormality-Driven Representation Learning for Radiology Imaging An Empirical Study of Training Self-Supervised Vision Transformers

Reference 8

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Observation 9032ff8f-116a-42ce-bf84-0e58ad76448a · outbound

This paper cites Momentum contrastive learning for few-shot covid- 19 diagnosis from chest ct images.Pattern Recognition, 113: 107826, 2021.

Abnormality-Driven Representation Learning for Radiology Imaging Momentum contrastive learning for few-shot covid- 19 diagnosis from chest ct images.Pattern Recognition, 113: 107826, 2021

Reference 9

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Observation e2569219-8fe2-4b56-896d-c9bcc187adaa · outbound

This paper cites Machine-learning-based multiple abnor- mality prediction with large-scale chest computed tomogra- phy volumes.

Abnormality-Driven Representation Learning for Radiology Imaging Machine-learning-based multiple abnor- mality prediction with large-scale chest computed tomogra- phy volumes

Reference 10

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Observation d109b972-0f5d-4870-8258-5281c19a8de7 · outbound

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Abnormality-Driven Representation Learning for Radiology Imaging Unresolved cited work

Reference 11

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Observation c5b2a0ad-5cc0-4165-9ab8-28a94fde4b20 · outbound

This paper cites Developing generalist foundation models from a multimodal dataset for 3d computed tomography, 2024.

Abnormality-Driven Representation Learning for Radiology Imaging Developing generalist foundation models from a multimodal dataset for 3d computed tomography, 2024

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:15:06.624396Z

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Observation ca3efc6a-5e47-4438-bbda-55d9a04f0133 · outbound

This paper cites Momentum contrast for unsupervised visual repre- sentation learning.

Abnormality-Driven Representation Learning for Radiology Imaging Momentum contrast for unsupervised visual repre- sentation learning

Reference 13

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Observation c3160abd-6658-4989-9580-b73adae0f04d · outbound

This paper cites Self- supervised learning for medical image classification: a sys- tematic review and implementation guidelines.

Abnormality-Driven Representation Learning for Radiology Imaging Self- supervised learning for medical image classification: a sys- tematic review and implementation guidelines

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:15:06.596952Z

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Observation 3ae7cbd7-a223-4c80-ac32-9426ee49fff5 · outbound

This paper cites Attention-based deep multiple instance learning.

Abnormality-Driven Representation Learning for Radiology Imaging Attention-based deep multiple instance learning

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:15:06.582615Z

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Observation aceb8026-86c0-4b8b-9efe-784d7967558c · outbound

This paper cites Gotway, and Jianming Liang.

Abnormality-Driven Representation Learning for Radiology Imaging Gotway, and Jianming Liang

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:15:06.568941Z

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Observation 93517a86-b649-42eb-8a8e-9a36ce5f20ca · outbound

This paper cites Chen, Sharifa Sahai, Dandan Mo, Emilio Madrigal, Long Phi Le, and Mahmood Faisal.

Abnormality-Driven Representation Learning for Radiology Imaging Chen, Sharifa Sahai, Dandan Mo, Emilio Madrigal, Long Phi Le, and Mahmood Faisal

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:15:06.554833Z

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Observation 41e837c9-29fa-493e-a343-cac572075f13 · outbound

This paper cites An mri deep learn- ing model predicts outcome in rectal cancer.

Abnormality-Driven Representation Learning for Radiology Imaging An mri deep learn- ing model predicts outcome in rectal cancer

Reference 18

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Observation 64163e60-98d1-4c58-98a3-1212f0d8e664 · outbound

This paper cites Predicting treat- ment response from longitudinal images using multi-task deep learning.

Abnormality-Driven Representation Learning for Radiology Imaging Predicting treat- ment response from longitudinal images using multi-task deep learning

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:15:06.526518Z

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Observation f83fe7b3-f93d-40e0-a741-b1648a52bbc9 · outbound

This paper cites Mimic-cxr, a de- identified publicly available database of chest radiographs with free-text reports.

Abnormality-Driven Representation Learning for Radiology Imaging Mimic-cxr, a de- identified publicly available database of chest radiographs with free-text reports

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:15:06.511017Z

Source-reported events for the cited work

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Observation 71959bf5-8bcb-4000-b620-bbc7c2afa5a8 · outbound

This paper cites A deep learning-based radiomics model for prediction of survival in glioblastoma multiforme.

Abnormality-Driven Representation Learning for Radiology Imaging A deep learning-based radiomics model for prediction of survival in glioblastoma multiforme

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:15:06.495288Z

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Observation a1f5916d-b8b9-45d5-aec9-8a6f48cf9cd4 · outbound

This paper cites Weakly supervised deep learning in radiology.

Abnormality-Driven Representation Learning for Radiology Imaging Weakly supervised deep learning in radiology

Reference 22

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Observation 1d15d54b-5517-4539-873d-3aa6cf2229ae · outbound

This paper cites Foundation model for cancer imaging biomarkers.

Abnormality-Driven Representation Learning for Radiology Imaging Foundation model for cancer imaging biomarkers

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:15:06.466795Z

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Observation aa920198-b96e-4415-82e9-73d6c39bcd43 · outbound

This paper cites A guide to artificial in- telligence for cancer researchers.

Abnormality-Driven Representation Learning for Radiology Imaging A guide to artificial in- telligence for cancer researchers

Reference 24

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Observation f49878e5-7eb7-480b-8a19-5f3d77c39f09 · outbound

This paper cites Prelaj et al.

Abnormality-Driven Representation Learning for Radiology Imaging Prelaj et al

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:15:06.439761Z

Source-reported events for the cited work

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Observation 3534830e-89b7-4e0f-bebc-7c02b1ac0919 · outbound

This paper cites Sam 2: Segment anything in images and videos,.

Abnormality-Driven Representation Learning for Radiology Imaging Sam 2: Segment anything in images and videos,

Reference 26

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Observation 812f4cf5-7381-4f40-a63c-00e1640b213c · outbound

This paper cites Repre- sentation learning with contrastive predictive coding, 2019.

Abnormality-Driven Representation Learning for Radiology Imaging Repre- sentation learning with contrastive predictive coding, 2019

Reference 27

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Source-reported events for the cited work

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Observation 07217908-fe85-4d02-9f5a-c52b3bb4dca4 · outbound

This paper cites Focal liver lesion diag- nosis with deep learning and multistage ct imaging.

Abnormality-Driven Representation Learning for Radiology Imaging Focal liver lesion diag- nosis with deep learning and multistage ct imaging

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:15:06.408975Z

Source-reported events for the cited work

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Observation be34e63c-e2e9-4bd7-9c31-1dc97baf5a0c · outbound

This paper cites Less is more: Selective reduction of ct data for self-supervised pre-training of deep learning models with contrastive learning improves downstream classification per- formance.

Abnormality-Driven Representation Learning for Radiology Imaging Less is more: Selective reduction of ct data for self-supervised pre-training of deep learning models with contrastive learning improves downstream classification per- formance

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:15:06.393310Z

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 4229be10-e751-414e-8912-d067d7a77c46 · outbound

This paper cites Unimiss: Universal medical self-supervised learning via breaking dimensionality barrier.

Abnormality-Driven Representation Learning for Radiology Imaging Unimiss: Universal medical self-supervised learning via breaking dimensionality barrier

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:15:06.378086Z

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Observation 8b383df1-5f15-4156-b813-dfc8b9ad2a4d · outbound

This paper cites Deeplesion: Automated mining of large-scale lesion annota- tions and universal lesion detection with deep learning.Jour- nal of Medical Imaging, 5(3):036501, 2018.

Abnormality-Driven Representation Learning for Radiology Imaging Deeplesion: Automated mining of large-scale lesion annota- tions and universal lesion detection with deep learning.Jour- nal of Medical Imaging, 5(3):036501, 2018

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:15:06.363740Z

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 111f2112-3221-4ea8-8439-e297c3dd56b6 · outbound

This paper cites an unresolved cited work.

Abnormality-Driven Representation Learning for Radiology Imaging Unresolved cited work

Reference 32

Resolution: unresolved; raw_fallback, observed 2026-08-12T13:15:06.349734Z

Source-reported events for the cited work

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Observation 07bac8a4-3905-428e-aaf8-437849cb1fe5 · outbound

This paper cites Lungren, Tristan Naumann, Sheng Wang, and Hoifung Poon.

Abnormality-Driven Representation Learning for Radiology Imaging Lungren, Tristan Naumann, Sheng Wang, and Hoifung Poon

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:15:06.335911Z

Source-reported events for the cited work

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Observation 2b75e2c0-bea3-443c-8a6a-f76ccedeb4aa · outbound

This paper cites Deep radiomics-based fusion model for prediction of bevacizumab treatment response and outcome in patients with colorectal cancer liver metastases: a multi- centre cohort study.

Abnormality-Driven Representation Learning for Radiology Imaging Deep radiomics-based fusion model for prediction of bevacizumab treatment response and outcome in patients with colorectal cancer liver metastases: a multi- centre cohort study

Reference 34

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Pith citing papers

No inbound Pith citation observations are available.