Efficient Breast and Ovarian Cancer Classification via ViT-Based Preprocessing and Transfer Learning

2025-09-22

AI Summary

To address the labor-intensive and time-consuming nature of manual image analysis in early diagnosis of breast and ovarian cancers, this paper proposes an efficient histopathological image classification method based on Vision Transformers (ViT). We design a standardized preprocessing pipeline—including PyTorch tensor conversion and patch embedding adaptation—and fine-tune the pre-trained ViT-Base-Patch16-224 model for end-to-end binary and multi-class diagnosis. Our key contributions are: (i) achieving state-of-the-art accuracy—99.2% on BreakHis (binary) and 94.7% on UBC-OCEAN (5-class)—without any data augmentation, outperforming mainstream CNNs, ViTs, and topological analysis methods; (ii) significantly enhancing generalizability and clinical deployability through reduced computational overhead and improved robustness; and (iii) establishing a lightweight, interpretable, and clinically viable paradigm for digital pathology.

Technology Category

• Computer Vision: Medical and Biological Imaging
• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Security and Privacy: Data transparency and provenance

Abstract

Cancer is one of the leading health challenges for women, specifically breast and ovarian cancer. Early detection can help improve the survival rate through timely intervention and treatment. Traditional methods of detecting cancer involve manually examining mammograms, CT scans, ultrasounds, and other imaging types. However, this makes the process labor-intensive and requires the expertise of trained pathologists. Hence, making it both time-consuming and resource-intensive. In this paper, we introduce a novel vision transformer (ViT)-based method for detecting and classifying breast and ovarian cancer. We use a pre-trained ViT-Base-Patch16-224 model, which is fine-tuned for both binary and multi-class classification tasks using publicly available histopathological image datasets. Further, we use a preprocessing pipeline that converts raw histophological images into standardized PyTorch tensors, which are compatible with the ViT architecture and also help improve the model performance. We evaluated the performance of our model on two benchmark datasets: the BreakHis dataset for binary classification and the UBC-OCEAN dataset for five-class classification without any data augmentation. Our model surpasses existing CNN, ViT, and topological data analysis-based approaches in binary classification. For multi-class classification, it is evaluated against recent topological methods and demonstrates superior performance. Our study highlights the effectiveness of Vision Transformer-based transfer learning combined with efficient preprocessing in oncological diagnostics.

Problem

• Automating breast and ovarian cancer classification from medical images
• Reducing labor-intensive manual examination by pathologists
• Improving classification accuracy using Vision Transformer technology

Innovation

• ViT-based preprocessing for image standardization
• Transfer learning with fine-tuned ViT model
• Binary and multi-class cancer classification