Meta-SVDD: Probabilistic Meta-Learning for One-Class Classification in Cancer Histology Images
This addresses the challenge of limited annotated abnormal samples in cancer histology for pathology experts, offering a practical tool for screening, though it appears incremental as it builds on existing meta-learning and one-class classification methods.
The paper tackles the problem of imbalanced medical datasets with scarce abnormal cases by proposing Meta-SVDD, a one-class classification model meta-trained on multiple histology datasets to classify cancer without expensive re-training, achieving applicability for screening purposes.
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.