aiqbench analysis
This research addresses the critical challenge of class imbalance in medical imaging, where standard deep learning models often favor common diseases over rare conditions due to skewed data distributions. By implementing advanced augmentation techniques and specialized architectures, the study demonstrates improved performance metrics like F1 score and AUROC for detecting underrepresented pathologies. These findings are significant for advancing equitable AI diagnostics in healthcare settings without relying on quantum computing or general consumer tech trends.
Long-Tailed Medical Image Classification
arXiv:2607.23883v1 Announce Type: cross Abstract: In this paper, we examine the difficulties of using standard techniques for medical image classification due to long-tailed distributions (wherein rarer conditions have very few samples) resulting in bias towards diagnosing common diseases and away from rarer diseases. We then discuss and implement deep learning models with techniques such as augmentation to minimize error, especially from rarer diseases. We evaluate various different models with AP, F1 score, AUROC, and loss (all on the validation set). We conclude with the promising results from our best model, and potential applications in the healthcare space.
Abstract:In this paper, we examine the difficulties of using standard techniques for medical image classification due to long-tailed distributions (wherein rarer conditions have very few samples) resulting in bias towards diagnosing common diseases and away from rarer diseases. We then discuss and implement deep learning models with techniques such as augmentation to minimize error, especially from rarer diseases. We evaluate various different models with AP, F1 score, AUROC, and loss (all on the validation set). We conclude with the promising results from our best model, and potential applications in the healthcare space.
Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
arXiv:2607.23883 [cs.CV]
(or arXiv:2607.23883v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.23883 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 192 words
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