Paper Citation Record · LEDGER

Benchmarking Robustness of Contrastive Learning Models for Medical Image-Report Retrieval

As of 23 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 1 inbound Pith citation observation for arXiv:2501.09134.

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

pith.paper-citation-record.v1
2501.09134 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T20:13:10.203962Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:21:17.873767Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T15:21:21.783758Z

Reference resolution

38 of 38 outbound references displayed

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External citation measurements

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Outbound references

Observation 57cbf030-cae6-43d9-b201-eeccfa2039cd · outbound

This paper cites Challenges and opportunities of big data in health care: a systematic review,.

Benchmarking Robustness of Contrastive Learning Models for Medical Image-Report Retrieval Challenges and opportunities of big data in health care: a systematic review,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:13:10.657034Z

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Observation 192224d0-9865-4837-befb-931cd85a03c6 · outbound

This paper cites A comprehensive survey of deep learning in the field of medical imaging and medical natural language processing: Challenges and research directions,.

Benchmarking Robustness of Contrastive Learning Models for Medical Image-Report Retrieval A comprehensive survey of deep learning in the field of medical imaging and medical natural language processing: Challenges and research directions,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:13:10.644947Z

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Observation 845cba51-157d-4dd7-9e7e-ff074f1c44c8 · outbound

This paper cites CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning.

Benchmarking Robustness of Contrastive Learning Models for Medical Image-Report Retrieval CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning

Reference 3

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Observation 3bf4a81a-2f2d-48b7-8823-c7db31a597bb · outbound

This paper cites Multi-modal data analysis for alzheimer’s disease diagnosis: An ensemble model using imagery and genetic features,.

Benchmarking Robustness of Contrastive Learning Models for Medical Image-Report Retrieval Multi-modal data analysis for alzheimer’s disease diagnosis: An ensemble model using imagery and genetic features,

Reference 4

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Observation 0215cfea-76f0-4997-8404-891ab2831506 · outbound

This paper cites Retrieval-based chest x-ray report generation using a pre-trained contrastive language-image model,.

Benchmarking Robustness of Contrastive Learning Models for Medical Image-Report Retrieval Retrieval-based chest x-ray report generation using a pre-trained contrastive language-image model,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:13:10.618989Z

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Observation 916699f7-7433-4f25-bf55-26682d21187f · outbound

This paper cites Medclip: Contrastive learning from unpaired medical images and text,.

Benchmarking Robustness of Contrastive Learning Models for Medical Image-Report Retrieval Medclip: Contrastive learning from unpaired medical images and text,

Reference 6

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Observation 6abdcb89-54e6-42ef-8b8c-d8966814c767 · outbound

This paper cites Cxr-clip: Toward large scale chest x-ray language-image pre-training,.

Benchmarking Robustness of Contrastive Learning Models for Medical Image-Report Retrieval Cxr-clip: Toward large scale chest x-ray language-image pre-training,

Reference 7

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Observation 11c0f347-b2ef-4e11-a0b3-c9c261a8402e · outbound

This paper cites Contrastive cross-modal pre-training: A general strategy for small sample medical imaging,.

Benchmarking Robustness of Contrastive Learning Models for Medical Image-Report Retrieval Contrastive cross-modal pre-training: A general strategy for small sample medical imaging,

Reference 8

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Observation 45fec27c-97c2-44fd-9cda-418aefc745b7 · outbound

This paper cites Learning transferable visual models from natural language supervision,.

Benchmarking Robustness of Contrastive Learning Models for Medical Image-Report Retrieval Learning transferable visual models from natural language supervision,

Reference 9

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Observation b0cca2ba-65e2-484e-ba2b-76b8353d7afb · outbound

This paper cites Enhancing machine learning based sql injection detection using contextualized word embedding,.

Benchmarking Robustness of Contrastive Learning Models for Medical Image-Report Retrieval Enhancing machine learning based sql injection detection using contextualized word embedding,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:13:10.562972Z

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Observation 69f10e0c-ff86-49e1-967e-5c4b9f560fb7 · outbound

This paper cites Enhancing neural text detector robustness with µ attacking and rr-training,.

Benchmarking Robustness of Contrastive Learning Models for Medical Image-Report Retrieval Enhancing neural text detector robustness with µ attacking and rr-training,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:13:10.550461Z

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Observation 2e353cc8-454d-465f-a625-46af0c377a17 · outbound

This paper cites Self-supervised learning application on covid-19 chest x-ray image classification using masked autoencoder,.

Benchmarking Robustness of Contrastive Learning Models for Medical Image-Report Retrieval Self-supervised learning application on covid-19 chest x-ray image classification using masked autoencoder,

Reference 12

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Observation 6bf12fca-9432-4d4e-8a36-d40a621ba534 · outbound

This paper cites Simulated quantum mechanics-based joint learning network for stroke lesion segmentation and tici grading,.

Benchmarking Robustness of Contrastive Learning Models for Medical Image-Report Retrieval Simulated quantum mechanics-based joint learning network for stroke lesion segmentation and tici grading,

Reference 13

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Observation 9fde024d-f754-4392-ba5e-972876251e75 · outbound

This paper cites Potential and challenges of large language models in public transportation: San antonio case study,.

Benchmarking Robustness of Contrastive Learning Models for Medical Image-Report Retrieval Potential and challenges of large language models in public transportation: San antonio case study,

Reference 14

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Observation 0c9bbc52-8b0d-4520-a6da-9faa35f04546 · outbound

This paper cites Unveiling roadway hazards: Enhancing fatal crash risk estimation through multiscale satellite imagery and self-supervised cross-matching,.

Benchmarking Robustness of Contrastive Learning Models for Medical Image-Report Retrieval Unveiling roadway hazards: Enhancing fatal crash risk estimation through multiscale satellite imagery and self-supervised cross-matching,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:13:10.500002Z

Source-reported events for the cited work

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Observation 9b647ca8-4270-432b-8667-30861e840566 · outbound

This paper cites A deep learning view of the census of galaxy clusters in illustristng,.

Benchmarking Robustness of Contrastive Learning Models for Medical Image-Report Retrieval A deep learning view of the census of galaxy clusters in illustristng,

Reference 16

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Observation 5fdfc315-cd56-4733-a600-1a081cfec8c5 · outbound

This paper cites Multi-branch attention networks for classifying galaxy clusters,.

Benchmarking Robustness of Contrastive Learning Models for Medical Image-Report Retrieval Multi-branch attention networks for classifying galaxy clusters,

Reference 17

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Observation aeac9b88-2b20-470f-97c9-37bd2e3309e0 · outbound

This paper cites Estimating cluster masses from sdss multiband images with transfer learning,.

Benchmarking Robustness of Contrastive Learning Models for Medical Image-Report Retrieval Estimating cluster masses from sdss multiband images with transfer learning,

Reference 18

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Observation cc2a6bde-e5e3-4e3a-b540-a3e0b17b2f63 · outbound

This paper cites On calibration of modern neural networks,.

Benchmarking Robustness of Contrastive Learning Models for Medical Image-Report Retrieval On calibration of modern neural networks,

Reference 19

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Observation 0cc5ce8c-b736-4637-aed2-b4c8b72019f4 · outbound

This paper cites Improved trainable calibration method for neural networks on medical imaging classification,.

Benchmarking Robustness of Contrastive Learning Models for Medical Image-Report Retrieval Improved trainable calibration method for neural networks on medical imaging classification,

Reference 20

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Observation 4e2e6efd-0d37-428b-a6ef-80f36bc8a5a1 · outbound

This paper cites Multi-scale probabilistic embedding for vision model calibration,.

Benchmarking Robustness of Contrastive Learning Models for Medical Image-Report Retrieval Multi-scale probabilistic embedding for vision model calibration,

Reference 21

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Observation f27cf3de-1922-4d78-99dc-ee063a3bf2ea · outbound

This paper cites Inconsistent performance of deep learning models on mammogram classification,.

Benchmarking Robustness of Contrastive Learning Models for Medical Image-Report Retrieval Inconsistent performance of deep learning models on mammogram classification,

Reference 22

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Observation 3e1f2194-101d-41dd-8bf2-6afd61fe9612 · outbound

This paper cites Neural network decision-making criteria consistency analysis via inputs sensitivity,.

Benchmarking Robustness of Contrastive Learning Models for Medical Image-Report Retrieval Neural network decision-making criteria consistency analysis via inputs sensitivity,

Reference 23

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This paper cites Adversarial attack vulnerability of medical image analysis systems: Unexplored factors,.

Benchmarking Robustness of Contrastive Learning Models for Medical Image-Report Retrieval Adversarial attack vulnerability of medical image analysis systems: Unexplored factors,

Reference 24

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Benchmarking Robustness of Contrastive Learning Models for Medical Image-Report Retrieval Defense-pointnet: Protecting pointnet against adversarial attacks,

Reference 25

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Observation f1b8e040-a61d-4aa2-9e10-a93e40df1800 · outbound

This paper cites Deepface: Closing the gap to human-level performance in face verification,.

Benchmarking Robustness of Contrastive Learning Models for Medical Image-Report Retrieval Deepface: Closing the gap to human-level performance in face verification,

Reference 26

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This paper cites Facenet: A unified embedding for face recognition and clustering,.

Benchmarking Robustness of Contrastive Learning Models for Medical Image-Report Retrieval Facenet: A unified embedding for face recognition and clustering,

Reference 27

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Observation 1abee39c-0a8a-4bd0-accd-18f0890c91c7 · outbound

This paper cites A simple framework for contrastive learning of visual representations,.

Benchmarking Robustness of Contrastive Learning Models for Medical Image-Report Retrieval A simple framework for contrastive learning of visual representations,

Reference 28

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Benchmarking Robustness of Contrastive Learning Models for Medical Image-Report Retrieval Momentum contrast for unsupervised visual representation learning,

Reference 29

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Observation 31b0cdb2-549e-4679-829a-1260e5e353b4 · outbound

This paper cites Siamese neural networks for one-shot image recognition,.

Benchmarking Robustness of Contrastive Learning Models for Medical Image-Report Retrieval Siamese neural networks for one-shot image recognition,

Reference 30

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Benchmarking Robustness of Contrastive Learning Models for Medical Image-Report Retrieval Mscnn: A monomeric-siamese convolutional neural network for extremely imbalanced multi-label text classification,

Reference 31

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This paper cites Deep residual learning for image recognition,.

Benchmarking Robustness of Contrastive Learning Models for Medical Image-Report Retrieval Deep residual learning for image recognition,

Reference 32

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

Benchmarking Robustness of Contrastive Learning Models for Medical Image-Report Retrieval An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 33

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Benchmarking Robustness of Contrastive Learning Models for Medical Image-Report Retrieval Bert: Pre-training of deep bidirectional transformers for language understanding,

Reference 34

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This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

Benchmarking Robustness of Contrastive Learning Models for Medical Image-Report Retrieval RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 35

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

Benchmarking Robustness of Contrastive Learning Models for Medical Image-Report Retrieval Mimic-cxr, a de-identified publicly available database of chest radiographs with free-text reports,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:13:10.283736Z

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This paper cites Negbio: a high-performance tool for negation and uncertainty detection in radiology reports,.

Benchmarking Robustness of Contrastive Learning Models for Medical Image-Report Retrieval Negbio: a high-performance tool for negation and uncertainty detection in radiology reports,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:13:10.271075Z

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Observation 4f5d8045-76c4-41ed-9ad5-fae24e1552a2 · outbound

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

Benchmarking Robustness of Contrastive Learning Models for Medical Image-Report Retrieval Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:13:10.259196Z

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Pith citing papers

Observation 915abbaa-6cd8-407d-94a5-815b401de255 · inbound

On the Robustness of Medical Vision-Language Models: Are they Truly Generalizable? cites this paper.

On the Robustness of Medical Vision-Language Models: Are they Truly Generalizable? Benchmarking Robustness of Contrastive Learning Models for Medical Image-Report Retrieval

Reference 4

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