Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance | Paper Citation Record

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This paper cites On model evalu- ation under non-constant class imbalance, in: International Conference on Computational Science, Springer.

Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance On model evalu- ation under non-constant class imbalance, in: International Conference on Computational Science, Springer

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This paper cites A study of why we need to reassess full reference image quality assessment with medical images.

Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance A study of why we need to reassess full reference image quality assessment with medical images

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This paper cites A study on the adequacy of com- mon iqa measures for medical images, in: Su, R., Frangi, A.F., Zhang, Y.

Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance A study on the adequacy of com- mon iqa measures for medical images, in: Su, R., Frangi, A.F., Zhang, Y

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This paper cites Padch- est: A large chest x-ray image dataset with multi-label annotated reports.

Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance Padch- est: A large chest x-ray image dataset with multi-label annotated reports

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This paper cites Why almost all ml models for medicine are wrong-and what we need for evidence-based medical ai.

Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance Why almost all ml models for medicine are wrong-and what we need for evidence-based medical ai

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This paper cites A simple frame- work for contrastive learning of visual representations, in: International conference on machine learning, PmLR.

Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance A simple frame- work for contrastive learning of visual representations, in: International conference on machine learning, PmLR

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This paper cites Towards unifying medical vision-and-language pre-training via soft prompts, in: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp.

Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance Towards unifying medical vision-and-language pre-training via soft prompts, in: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp

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This paper cites A coefficient of agreement for nominal scales.

Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance A coefficient of agreement for nominal scales

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This paper cites Weighted kappa: Nominal scale agreement provision for scaled disagreement or partial credit.

Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance Weighted kappa: Nominal scale agreement provision for scaled disagreement or partial credit

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This paper cites On the limits of cross-domain generalization in automated x-ray prediction, in: Medical Imaging with Deep Learning, PMLR.

Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance On the limits of cross-domain generalization in automated x-ray prediction, in: Medical Imaging with Deep Learning, PMLR

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Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance Unresolved cited work

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This paper cites The relationship between precision-recall and roc curves, in: Proceedings of the 23rd international conference on Machine learning, pp.

Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance The relationship between precision-recall and roc curves, in: Proceedings of the 23rd international conference on Machine learning, pp

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This paper cites Maximum likelihood estimation of ob- server error-rates using the EM algorithm.

Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance Maximum likelihood estimation of ob- server error-rates using the EM algorithm

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This paper cites Preparing a collection of radiology examinations for distribution and retrieval.

Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance Preparing a collection of radiology examinations for distribution and retrieval

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This paper cites An image is worth 16x16 words: Transformers for image recognition at scale, in: International Conference on Learning Representations.

Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance An image is worth 16x16 words: Transformers for image recognition at scale, in: International Conference on Learning Representations

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Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy

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Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance Unresolved cited work

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Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance Digital Image Processing

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This paper cites Evaluating the robustness and readiness of large frontier models in health ai applications.

Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance Evaluating the robustness and readiness of large frontier models in health ai applications

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This paper cites Robust classification from noisy labels: Integrating additional knowledge for chest radiography abnormality assessment.

Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance Robust classification from noisy labels: Integrating additional knowledge for chest radiography abnormality assessment

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Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance The meaning and use of the area under a receiver operating characteristic (roc) curve

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Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance Deep residual learning for image recognition, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp

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Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance Unresolved cited work

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Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance Densely connected convolutional networks, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp

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Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison

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Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance Parameter choices in 32 haarpsiforiqawithmedicalimages, in: 2025IEEE22ndInternationalSym- posium on Biomedical Imaging (ISBI), pp

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Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance A new measure of rank corre- lation

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Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance Chest radiograph interpretation with deep learning models: as- sessment with radiologist-adjudicated reference standards and population- adjusted evaluation

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Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance completely blind

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Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance Vindr-cxr: An open dataset of chest x-rays with radiologist’s annotations

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Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance Representation Learning with Contrastive Predictive Coding

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Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation

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Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance Deep learning for chest radiograph diagnosis: A retrospective comparison of the chexnext algorithm to practicing radiologists

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Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance A haar wavelet-basedperceptualsimilarityindexforimagequalityassessment

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Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance Common pitfalls and recommendations for using machine learning to detect and prognosticate for covid-19 using chest radiographs and ct scans

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Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance The sankey diagram in energy and material flow man- agement: part ii: methodology and current applications

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Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance Speedy iqa for desktop: An image viewer and labeller for image quality assessment (iqa).https://github.com/selbs/speedy_iqa

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Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance Scientific Data 9, 487

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Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance The proof and measurement of association between two things

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Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance Multi-granularity cross-modal alignment for gener- alized medical visual representation learning, in: Advances in Neural Information Processing Systems, pp

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Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance Speedy qc: Customisable annotation tool for medical images.https://github.com/selbs/speedy_qc

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Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance MedCLIP: Con- trastive learning from unpaired medical images and text, in: Goldberg, Y., Kozareva, Z., Zhang, Y

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Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance The effect of class imbalance on precision-recall curves

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Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance Weakly Supervised Lesion Localization With Probabilistic-CAM Pooling

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Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance Fsim: A feature similarity indexforimagequalityassessment

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Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance Image quality assessment: from error visibility to structural similarity

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Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance Gen- eralized radiograph representation learning via cross-supervision between images and free-text radiology reports

Reference 57

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This paper cites Advancing radiograph representation learning with masked record modeling, in: The Eleventh International Conference on Learning Representations (ICLR).

Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance Advancing radiograph representation learning with masked record modeling, in: The Eleventh International Conference on Learning Representations (ICLR)

Reference 58

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Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance Unresolved cited work

Reference 59

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This paper cites Contrastive learning of medical visual representations from paired images and text, in: Proceedings of the 7th Machine Learning for Healthcare Con- ference, PMLR.

Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance Contrastive learning of medical visual representations from paired images and text, in: Proceedings of the 7th Machine Learning for Healthcare Con- ference, PMLR

Reference 62

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Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance Unresolved cited work

Reference 64

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Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance Advances in Neural Information Processing Systems 37, 6625–6647

Reference 67

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Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases

Reference 2017

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Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance URL:https://physionet.org/content/ mimic-cxr/, doi:10.13026/C2JT1Q

Reference 2019

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Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance Unresolved cited work

Reference 2021

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Reference 2022

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Reference 2024

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