AI-Based Whole Slide Image Analysis for Automated Breast Cancer Classification

Abstract

Breast cancer remains a global health challenge, representing one of the most prevalent and deadly cancers worldwide. Histopathological analysis of biopsy specimens is the gold standard for diagnosis, but the increasing demand for assessments often surpasses the capacity of available pathologists. To address this challenge, this study proposes a lightweight framework for breast cancer classification from whole-slide images (WSIs) that decouples feature extraction, tile-to-slide aggregation, and classification. Tile-level descriptors are extracted using a pre-trained VGG19 network modified with a configurable max pooling layer that controls descriptor dimensionality. The resulting tile embeddings are summarized into compact slide-level representations by Incremental Uniform Attention (IUA), a non-parametric, permutation-invariant aggregation operator with linear time complexity and no trainable parameters. The slide descriptors are then classified using six conventional machine learning models. The framework was evaluated on $3{,}241$ WSIs from the TCGA-BRCA and HCMI-CMDC cohorts using a nested cross-validation protocol. In its most compact configuration, the pipeline reduced the stored feature representation from approximately 1.7 TB to 64 MB, corresponding to a reduction of more than $26{,}000\times $ , while preserving high predictive performance. Support Vector Machine yielded the most stable results across pooling configurations, whereas Logistic Regression achieved the highest overall performance in the most compact setting, with 99.7% accuracy, 99.8% F1-score, and 99.99% AUC. A theoretical analysis further shows that IUA has linear time and auxiliary-memory complexity with respect to the number of tiles per slide, in contrast to the quadratic complexity of self-attention-based Multiple Instance Learning (MIL) aggregators. These results indicate that compact, parameter-free aggregation can substantially reduce computational and storage costs, enabling large-scale WSI cohort analysis on conventional hardware.