CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning | Paper Citation Record

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This paper cites Siim-acr pneumothorax seg- mentation.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning Siim-acr pneumothorax seg- mentation

Reference 1

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Observation 9b5da6fb-dd8f-4d25-bc0e-2510200f4a21 · outbound

This paper cites Self-supervised learning from images with a joint-embedding predictive architecture.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning Self-supervised learning from images with a joint-embedding predictive architecture

Reference 2

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This paper cites Big self-supervised models advance medical image classifica- tion.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning Big self-supervised models advance medical image classifica- tion

Reference 3

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This paper cites Data2vec: A general frame- work for self-supervised learning in speech, vision and lan- guage.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning Data2vec: A general frame- work for self-supervised learning in speech, vision and lan- guage

Reference 4

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This paper cites Efficient self-supervised learning with contextualized target representations for vision, speech and language.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning Efficient self-supervised learning with contextualized target representations for vision, speech and language

Reference 5

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This paper cites BEiT: BERT Pre-Training of Image Transformers.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning BEiT: BERT Pre-Training of Image Transformers

Reference 6

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This paper cites V-jepa: Latent video prediction for visual represen- tation learning.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning V-jepa: Latent video prediction for visual represen- tation learning

Reference 7

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This paper cites High Fidelity Visualization of What Your Self-Supervised Representation Knows About.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning High Fidelity Visualization of What Your Self-Supervised Representation Knows About

Reference 8

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Observation d6ba0968-a166-4f2f-94b0-255277bfef2b · outbound

This paper cites Applied optimal control: optimization, estimation and control.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning Applied optimal control: optimization, estimation and control

Reference 9

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Observation afb85996-ca31-402d-a07c-a442c67b5b9e · outbound

This paper cites Constrained model predictive control.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning Constrained model predictive control

Reference 10

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Observation 1d504471-482f-4109-bda0-c63a464ba251 · outbound

This paper cites Emerg- ing properties in self-supervised vision transformers.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning Emerg- ing properties in self-supervised vision transformers

Reference 11

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Observation 7e94ccb4-f81e-4fbc-8520-906af1939333 · outbound

This paper cites Contrastive learning of global and local fea- tures for medical image segmentation with limited annota- tions.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning Contrastive learning of global and local fea- tures for medical image segmentation with limited annota- tions

Reference 12

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Observation 085c954b-5190-4366-b17f-2d56fecd12e3 · outbound

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

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning Towards a general-purpose foundation model for computational pathology

Reference 13

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Observation ec526f5d-d81a-4579-aff1-2c76b7d0d14f · outbound

This paper cites An empirical study of training self-supervised vision transformers.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning An empirical study of training self-supervised vision transformers

Reference 14

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This paper cites Confidence- based reliable learning under dual noises.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning Confidence- based reliable learning under dual noises

Reference 15

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This paper cites Equivariant Contrastive Learning.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning Equivariant Contrastive Learning

Reference 16

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Observation 187751a7-1217-4141-8d42-ff4cccff7f24 · outbound

This paper cites Equimod: An equivariance module to improve visual instance discrimina- tion.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning Equimod: An equivariance module to improve visual instance discrimina- tion

Reference 17

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Observation a09a6cfa-cf5d-43cc-8788-f475508c1c26 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 18

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Observation 98d98a58-20ee-4717-8524-9fa4ea9c85b3 · outbound

This paper cites Prob- abilistic contrastive learning for long-tailed visual recogni- tion.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning Prob- abilistic contrastive learning for long-tailed visual recogni- tion

Reference 19

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Observation f1fb25ec-ae00-42c5-8f9a-30edf875e84d · outbound

This paper cites A Learned Representation For Artistic Style.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning A Learned Representation For Artistic Style

Reference 20

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This paper cites Synthetic data accelerates the development of gener- alizable learning-based algorithms for x-ray image analysis.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning Synthetic data accelerates the development of gener- alizable learning-based algorithms for x-ray image analysis

Reference 21

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Observation 34ea3945-0829-4e79-9b41-90df8755c29a · outbound

This paper cites Learning and Leveraging World Models in Visual Representation Learning.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning Learning and Leveraging World Models in Visual Representation Learning

Reference 22

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Observation b2eab51a-b6d7-497b-9b8c-f17179458979 · outbound

This paper cites Rich feature hierarchies for accurate object detection and semantic segmentation.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning Rich feature hierarchies for accurate object detection and semantic segmentation

Reference 23

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Observation af3cfdb8-185f-411b-9247-4d50348c4e29 · outbound

This paper cites Domain adaptation for medical image analysis: a survey.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning Domain adaptation for medical image analysis: a survey

Reference 24

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Observation 565f45cc-0372-4807-a338-7ffc8812d294 · outbound

This paper cites Recurrent world models facilitate policy evolution.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning Recurrent world models facilitate policy evolution

Reference 25

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This paper cites World Models.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning World Models

Reference 26

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This paper cites Dream to Control: Learning Behaviors by Latent Imagination.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning Dream to Control: Learning Behaviors by Latent Imagination

Reference 27

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This paper cites Mastering Atari with Discrete World Models.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning Mastering Atari with Discrete World Models

Reference 28

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This paper cites Mastering Diverse Domains through World Models.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning Mastering Diverse Domains through World Models

Reference 29

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Observation d5ebcddc-3bfa-4ae8-89c4-b37b4d7e06e3 · outbound

This paper cites Transferable visual words: Exploiting the semantics of anatomical patterns for self-supervised learning.IEEE trans- actions on medical imaging, 40(10):2857–2868, 2021.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning Transferable visual words: Exploiting the semantics of anatomical patterns for self-supervised learning.IEEE trans- actions on medical imaging, 40(10):2857–2868, 2021

Reference 30

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Observation cc19ce1d-b53e-4dc3-8cf5-01572cc9faf3 · outbound

This paper cites Dira: Discrimina- tive, restorative, and adversarial learning for self-supervised medical image analysis.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning Dira: Discrimina- tive, restorative, and adversarial learning for self-supervised medical image analysis

Reference 31

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Observation 9963e5df-8490-404d-bb14-edc4da13f243 · outbound

This paper cites Masked autoencoders are scalable vision learners.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning Masked autoencoders are scalable vision learners

Reference 32

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Observation b6e77c30-e8b6-433d-a8ca-05affee893f9 · outbound

This paper cites Representing part-whole hierarchies in foundation models by learning localizability, composability, and decomposability from anatomy via self-supervision.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning Representing part-whole hierarchies in foundation models by learning localizability, composability, and decomposability from anatomy via self-supervision

Reference 33

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Observation 851e7342-04bd-490a-96d4-92b6889a9e76 · outbound

This paper cites GAIA-1: A Generative World Model for Autonomous Driving.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning GAIA-1: A Generative World Model for Autonomous Driving

Reference 34

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Observation a670897a-b5af-4d65-935b-a857ea769fce · outbound

This paper cites A visual–language foundation model for pathology image analysis using medical twitter.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning A visual–language foundation model for pathology image analysis using medical twitter

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Observation 5a29e7ad-4348-4bc7-8ec5-396193a64459 · outbound

This paper cites Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison

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Observation 50b6da5e-e38e-456e-a1be-6c657f8e5c56 · outbound

This paper cites Two public chest x- ray datasets for computer-aided screening of pulmonary dis- eases.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning Two public chest x- ray datasets for computer-aided screening of pulmonary dis- eases

Reference 37

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Observation e555a4ac-fa74-482d-9cb8-9b8b26ed6553 · outbound

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

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning Mimic-cxr, a de- identified publicly available database of chest radiographs with free-text reports

Reference 38

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Observation ae25a321-cb42-4271-bdab-eadacd755bf3 · outbound

This paper cites How Far is Video Generation from World Model: A Physical Law Perspective.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning How Far is Video Generation from World Model: A Physical Law Perspective

Reference 39

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Observation 05a80441-855e-4f81-a0d5-25ff40ff4f44 · outbound

This paper cites VideoPoet: A Large Language Model for Zero-Shot Video Generation.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning VideoPoet: A Large Language Model for Zero-Shot Video Generation

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Observation 1c7a90e6-2245-4a0c-9865-fe9472a0b04d · outbound

This paper cites A path towards autonomous machine intelli- gence version 0.9.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning A path towards autonomous machine intelli- gence version 0.9

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Observation 6ccd0b33-de5d-45e8-abec-d9ba8d7e39f0 · outbound

This paper cites Exploring plain vision transformer backbones for object de- tection.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning Exploring plain vision transformer backbones for object de- tection

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Observation 9c6caf61-8aeb-469b-8198-08ca48dd5e22 · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning Swin transformer: Hierarchical vision transformer using shifted windows

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Observation 5ad819de-3774-49d5-9a43-98ea851c97b3 · outbound

This paper cites A convnet for the 2020s.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning A convnet for the 2020s

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Observation c7f6652a-3cc5-4ca7-b465-f25a7b289a1a · outbound

This paper cites Fixing weight decay reg- ularization in adam.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning Fixing weight decay reg- ularization in adam

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Observation 12fbf237-1739-4dac-b716-a4b87d3269fd · outbound

This paper cites Benchmarking and boosting transformers for medical image classification.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning Benchmarking and boosting transformers for medical image classification

Reference 46

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This paper cites Case courtesy of phillip marsh, radiopae- dia.org.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning Case courtesy of phillip marsh, radiopae- dia.org

Reference 47

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Observation 7fcb65a5-3c02-427e-bbe9-0a7b1b6fc5d7 · outbound

This paper cites Lvm-med: Learning large- scale self-supervised vision models for medical imaging via second-order graph matching.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning Lvm-med: Learning large- scale self-supervised vision models for medical imaging via second-order graph matching

Reference 48

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This paper cites Foundation models for generalist medi- cal artificial intelligence.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning Foundation models for generalist medi- cal artificial intelligence

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This paper cites Vindr-cxr: An open dataset of chest x-rays with radiologist’s annotations.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning Vindr-cxr: An open dataset of chest x-rays with radiologist’s annotations

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:04:32.841185Z

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Observation 5a3ab5e2-630b-4da4-90db-0c4e5f64c23b · outbound

This paper cites Video generation models as world simula- tors.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning Video generation models as world simula- tors

Reference 51

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This paper cites Dinov2: Learning robust visual features without supervision.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning Dinov2: Learning robust visual features without supervision

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Observation ad56098e-dcff-48a8-b8a3-698e4bc1c1ed · outbound

This paper cites Causality-inspired single- source domain generalization for medical image segmenta- tion.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning Causality-inspired single- source domain generalization for medical image segmenta- tion

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Observation 807f28be-7d20-49a0-a032-e278b1a10a05 · outbound

This paper cites Popar: Patch order prediction and appearance recovery for self-supervised medical image anal- ysis.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning Popar: Patch order prediction and appearance recovery for self-supervised medical image anal- ysis

Reference 54

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Observation 6998824e-ca7a-4cb4-b3ca-015115f90f36 · outbound

This paper cites Exploring scalable medical image encoders beyond text supervision.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning Exploring scalable medical image encoders beyond text supervision

Reference 55

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This paper cites Why don’t radiology textbooks have imperfect images? https://www.diagnosticimaging.com/ view/why- dont- radiology- textbooks- have- imperfect-images, 2019.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning Why don’t radiology textbooks have imperfect images? https://www.diagnosticimaging.com/ view/why- dont- radiology- textbooks- have- imperfect-images, 2019

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:04:32.749793Z

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Observation a6841ddb-c8af-4b15-b373-e8765dd2c638 · outbound

This paper cites Introducing general world models.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning Introducing general world models

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:04:32.734600Z

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Observation be433195-d72f-4b3a-9990-a1b855025860 · outbound

This paper cites Augmenting the national institutes of health chest radiograph dataset with expert annotations of possi- ble pneumonia.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning Augmenting the national institutes of health chest radiograph dataset with expert annotations of possi- ble pneumonia

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:04:32.718986Z

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Observation 8e45c60d-b2c6-47d2-b4ea-2ae81427d9ce · outbound

This paper cites Neu- ral representational geometry underlies few-shot concept learning.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning Neu- ral representational geometry underlies few-shot concept learning

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:04:32.702271Z

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This paper cites Moco pretraining improves representation and transferability of chest x-ray models.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning Moco pretraining improves representation and transferability of chest x-ray models

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:04:32.684920Z

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This paper cites Caid: a self- supervised learning framework for empowering instance dis- crimination in medical imaging.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning Caid: a self- supervised learning framework for empowering instance dis- crimination in medical imaging

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:04:32.668590Z

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Observation 11c674d5-3ed3-42f8-ab54-72044ca16dd3 · outbound

This paper cites Revisiting rubik’s cube: self-supervised learning with volume-wise transformation for 3d medical image seg- mentation.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning Revisiting rubik’s cube: self-supervised learning with volume-wise transformation for 3d medical image seg- mentation

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:04:32.650595Z

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This paper cites Attention is all you need.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning Attention is all you need

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:04:32.634132Z

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Observation cfb50f89-89c7-451e-9cfa-fa14ad3b7aa7 · outbound

This paper cites A real-world dataset and benchmark for foundation model adaptation in medical image classification.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning A real-world dataset and benchmark for foundation model adaptation in medical image classification

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:04:32.617624Z

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This paper cites Chestx- ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning Chestx- ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases

Reference 65

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Observation 21e0d7c3-fd08-441a-96af-ec77b203504c · outbound

This paper cites Efficienttrain: Exploring gener- alized curriculum learning for training visual backbones.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning Efficienttrain: Exploring gener- alized curriculum learning for training visual backbones

Reference 66

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Observation cf6fd4e9-8dab-4c19-800b-8acdf45fe2e2 · outbound

This paper cites Delving into masked autoencoders for multi-label thorax dis- ease classification.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning Delving into masked autoencoders for multi-label thorax dis- ease classification

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:04:32.579097Z

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This paper cites Simmim: A simple framework for masked image modeling.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning Simmim: A simple framework for masked image modeling

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:04:32.562631Z

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Observation 27cdf427-035b-4b87-95db-1cef0cd4b159 · outbound

This paper cites Sam: Self-supervised learning of pixel-wise anatom- ical embeddings in radiological images.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning Sam: Self-supervised learning of pixel-wise anatom- ical embeddings in radiological images

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:04:32.545563Z

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Observation f8eec6c4-ebfb-4445-9dea-5c9f059190a6 · outbound

This paper cites Comparing to learn: Surpassing ima- genet pretraining on radiographs by comparing image repre- sentations.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning Comparing to learn: Surpassing ima- genet pretraining on radiographs by comparing image repre- sentations

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:04:32.528004Z

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Observation 65904bf9-f251-44cd-b5fd-d4c1f46e01ec · outbound

This paper cites Self pre-training with masked autoencoders for medical image classification and segmentation.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning Self pre-training with masked autoencoders for medical image classification and segmentation

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:04:32.511267Z

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Observation b98326cb-3be2-42cd-87dd-29acc17f76ef · outbound

This paper cites A foundation model for generalizable disease detection from retinal images.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning A foundation model for generalizable disease detection from retinal images

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-16T12:04:32.079881Z

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This paper cites Models genesis.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning Models genesis

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:04:32.482431Z

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Observation 2e15f26f-0492-4992-9dfe-51a7ea801c4c · outbound

This paper cites Learning Anatomically Consistent Embedding for Chest Radiography.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning Learning Anatomically Consistent Embedding for Chest Radiography

Reference 74

Resolution: verified exact; local_arxiv, observed 2026-08-16T12:04:32.172831Z

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This paper cites Detailed information on these datasets is provided below.

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning Detailed information on these datasets is provided below

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:04:32.464353Z

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CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning Unresolved cited work

Reference 2024

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