Attention-Based Deep Learning System for Classification of Breast Lesions-Multimodal, Weakly Supervised Approach.

ABSTRACT: Breast cancer is the most frequent female cancer, with a considerable disease burden and high mortality. Early diagnosis with screening mammography might be facilitated by automated systems supported by deep learning artificial intelligence. We propose a model based on a weakly supervised Clustering-constrained Attention Multiple Instance Learning (CLAM) classifier able to train under data scarcity effectively. We used a private dataset with 1174 non-cancer and 794 cancer images labelled at the image level with pathological ground truth confirmation. We used feature extractors (ResNet-18, ResNet-34, ResNet-50 and EfficientNet-B0) pre-trained on ImageNet. The best results were achieved with multimodal-view classification using both CC and MLO images simultaneously, resized by half, with a p

SUBMITTER: Bobowicz M 

PROVIDER: S-EPMC10216803 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature