# Can Score-Based Generative Modeling Effectively Handle Medical Image Classification? Source: [Can Score-Based Generative Modeling Effectively Handle Medical Image Classification?]() Collection index: [llms.txt]() Data updated: 2025-10-26T04:09:56.744Z Authors: Sushmita Sarker, Prithul Sarker, George Bebis, Alireza Tavakkoli Published: 2025-02-24T23:41:33.000Z ## Abstract The remarkable success of deep learning in recent years has prompted applications in medical image classification and diagnosis tasks. While classification models have demonstrated robustness in classifying simpler datasets like MNIST or natural images such as ImageNet, this resilience is not consistently observed in complex medical image datasets where data is more scarce and lacks diversity. Moreover, previous findings on natural image datasets have indicated a potential trade-off between data likelihood and classification accuracy. In this study, we explore the use of score-based generative models as classifiers for medical images, specifically mammographic images. Our findings suggest that our proposed generative classifier model not only achieves superior classification results on CBIS-DDSM, INbreast and Vin-Dr Mammo datasets, but also introduces a novel approach to image classification in a broader context. Our code is publicly available at https://github.com/sushmitasarker/sgc\_for\_medical\_image\_classification ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()