Dual-Level Imbalance Mitigation for Single-FoV Colorectal Histopathology Image Classification.

Abstract

Single-field-of-view (FoV) histopathological image classification is vital for colorectal cancer (CRC) diagnosis in mid- to low-tier hospitals lacking whole-slide imaging (WSI) scanners and storage, yet suffers from severe class imbalance and degraded performance. To address this, we propose a dual-level imbalance mitigation (DIM) framework integrating data-level and algorithm-level approaches. Specifically: (1) A global context generative adversarial network (GCGAN) generates realistic minority-class images for augmentation to balance the dataset. (2) A frequency-aware adaptive focal loss (FAFL) applies a frequency-aware offset and adaptive modulation to better separate overlapping classes. (3) A lightweight receptive field-based convolutional neural network (LRF-CNN) is trained under DIM to leverage both augmentation and loss modulation for improved classification. Extensive experiments on the single-FoV colorectal histopathology dataset demonstrate that DIM-equipped LRF-CNN outperforms five state-of-the-art models (SOTA) across multiple metrics. Furthermore, each DIM component enhances performance when applied individually to those SOTA models, and additional validation on six single-FoV histopathological datasets confirms the generalizability and effectiveness of the proposed DIM framework. Our code is available at https://github.com/Lingling-Yuan/DIM.