Deep Attention-Driven Multi-Scale AI Framework for Automated Breast Histopathology Analysis

Abstract

Breast cancer diagnosis based on histopathological examination is a critical process but a very time-consuming one, which requires expert interpretation, commonly hampered by inter-observer variability and high complexity of tissue heterogeneity. To overcome these limitations, this study recommends a strong deep learning framework for automatic analysis of breast histopathology images with multi-scale convolutional Neural Networks(CNN)-Transformer feature extractor. The main goal is to improve the diagnostic accuracy, interpretability and clinical applicability with an end-to-end workflow combining the digitization, preprocessing, multi-scale feature extraction, attention-guided tissue classification and intelligent reporting of a whole slide image (WSI). The methodology utilizes stain normalization, artifact correction, as well as region of interest extraction that are followed by a hybrid CNN-Transformer model based on the extraction of both local cellular morphology and global tissue context. Attention mechanisms are dedicated to diagnostically interesting areas, and an ongoing learning cycle allows the adaptation of models based on the feedback provided by the pathologist. Experimental validation using BreaKHis dataset containing multi magnification images of breast histopathology (40x-400x) showed high performance of the proposed framework with 94.2% accuracy, 0.96 AUC, 0.92 F1-score, 93.5% sensitivity and 94.8% specificity as compared to benchmark models such as SAMASK-CLTR and ResNet-SVM. Overall, the results confirm that the proposed multi-scale artificial intelligence (AI) pathology framework is an efficient, explainable and scalable solution to improve the precision of diagnoses and assist clinical decision-making in digital breast pathology.

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