Meta-SVDD: Probabilistic Meta-Learning for One-Class Classification in Cancer Histology Images
Jevgenij Gamper, Brandon W. Chan, Yee‐Wah Tsang, David R. J. Snead, Nasir Rajpoot · arXiv (Cornell University) · 2020
To train a robust deep learning model, one usually needs a balanced set of categories in the training data. The data acquired in a medical domain, however, frequently contains an abundance of healthy patients, versus a small variety of positive, abnormal cases. Moreover, the annotation of a positive sample requires time consuming input from medical domain experts. This scenario would suggest a promise for one-class classification type approaches. In this work we propose a general one-class classification model for histology, that is meta-trained on multiple histology datasets simultaneously, and can be applied to new tasks without expensive re-training. This model could be easily used by pathology domain experts, and potentially be used for screening purposes.