Multi-Scale Deep Learning Models for Pathology Image Analysis

Abstract

Tumors exhibit significant morphological heterogeneity at the macroscopic, histological, and cellular levels, which complicates their analysis. Pathological examination remains the gold standard for cancer diagnosis, but manual microscopic evaluation of whole slide images (WSIs) is time-consuming, labor-intensive, and prone to error. Recent advancements in digital imaging have led to the emergence of computational histopathology, which aims to enhance clinical workflows by leveraging advanced image processing and analysis techniques.

Early computational approaches relied on manually extracted, handcrafted features from histopathological images, which were limited in their generalizability and robustness. The advent of artificial intelligence (AI), particularly deep learning (DL), has transformed this process by automating feature extraction, improving model accuracy, and enhancing the efficiency of cellular and tissue-level analysis. These techniques have demonstrated significant potential in supporting cancer diagnosis and prognosis.

However, significant challenges remain in capturing and interpreting the morphological heterogeneity across different scales, from individual cells to larger tissue structures.