Can Score-Based Generative Modeling Effectively Handle Medical Image Classification?
This work addresses the problem of robust classification for medical professionals in scenarios with scarce and less diverse data, representing an incremental advancement by adapting existing generative methods to a new domain.
The study tackled the challenge of medical image classification by applying score-based generative models to mammographic images, achieving superior classification results on CBIS-DDSM, INbreast, and Vin-Dr Mammo datasets.
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