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Multiple-Instance Learning with Efficient Transformer for Breast Tumor Image Classification in Bright Challenge

A computational workflow based on multiple-instance learning (MIL) and an efficient transformer was established to classify breast cancer subtypes from hematoxylin-and-eosin images. A tissue segmentation model was trained to identify only ductal epithelium patches for feature embedding and classification. Two feature extractors using self-supervised learning and fully supervised pretraining were tested together with a method based on MIL and a transformer on the dataset provided in the BRIGHT Challenge. The best approach achieved average F1 scores of 0.750 and 0.503 on the validation set for the two tasks, although the performance degraded to 0.643 and 0.388, respectively, on the final test set.