Towards Human-AI Collaboration System for the Detection of Invasive Ductal Carcinoma in Histopathology Images

TL;DR

This paper presents a human-in-the-loop deep learning system that combines AI and medical expertise to improve the detection accuracy of invasive ductal carcinoma in histopathology images, achieving state-of-the-art results.

Contribution

It introduces a collaborative framework that iteratively refines IDC detection models through human feedback, enhancing accuracy over traditional AI methods.

Findings

EfficientNetV2S model achieves 93.65% accuracy in IDC detection.

Human-in-the-loop process improves model performance with iterative feedback.

The system demonstrates potential for more accurate AI-assisted medical diagnosis.

Abstract

Invasive ductal carcinoma (IDC) is the most prevalent form of breast cancer, and early, accurate diagnosis is critical to improving patient survival rates by guiding treatment decisions. Combining medical expertise with artificial intelligence (AI) holds significant promise for enhancing the precision and efficiency of IDC detection. In this work, we propose a human-in-the-loop (HITL) deep learning system designed to detect IDC in histopathology images. The system begins with an initial diagnosis provided by a high-performance EfficientNetV2S model, offering feedback from AI to the human expert. Medical professionals then review the AI-generated results, correct any misclassified images, and integrate the revised labels into the training dataset, forming a feedback loop from the human back to the AI. This iterative process refines the model's performance over time. The EfficientNetV2S model itself achieves state-of-the-art performance compared to existing methods in the literature, with an overall accuracy of 93.65\%. Incorporating the human-in-the-loop system further improves the model's accuracy using four experimental groups with misclassified images. These results demonstrate the potential of this collaborative approach to enhance AI performance in diagnostic systems. This work contributes to advancing automated, efficient, and highly accurate methods for IDC detection through human-AI collaboration, offering a promising direction for future AI-assisted medical diagnostics.