Robust ROI Detection in Whole Slide Images Guided by Pathologists' Viewing Patterns.

Area of Science:

• Digital pathology
• Artificial intelligence in medicine
• Computer-aided diagnosis

Background:

• Pathologist expertise is crucial for accurate diagnosis from whole slide images.
• Annotation of critical regions in pathology images is time-consuming and requires specialized knowledge.
• Deep learning models benefit from understanding pathologist viewing patterns to focus on diagnostically relevant areas.

Purpose of the Study:

• To develop a deep learning system that integrates pathologist viewing patterns (heatmaps) to guide region of interest detection.
• To improve the performance of computer-aided diagnosis systems by leveraging domain expertise.
• To reduce the need for manual annotations in training deep learning models for pathology.

Main Methods:

• Generating heatmaps from pathologist viewing data to represent diagnostic focus areas.
• Training a U-Net deep learning model with a ResNet-18 encoder, guided by these heatmaps.
• Evaluating the model on a skin biopsy dataset for melanoma diagnosis and comparing it to traditional methods.
• Conducting a clinical evaluation with dermatopathologists to assess the model's performance and relevance.

Main Results:

• The proposed system demonstrated superior performance over traditional methods, with significant increases in precision (20%), recall (11%), F1-score (22%), and Intersection over Union (12%).
• The U-Net model effectively identified regions of interest, mimicking pathologist diagnostic behavior.
• Clinical evaluation confirmed the model's ability to replicate expert viewing patterns and identify critical diagnostic regions.

Conclusions:

• Incorporating heatmaps of pathologist viewing patterns as supplementary signals enhances deep learning-based computer-aided diagnosis systems.
• This approach shows promise for improving diagnostic accuracy and efficiency in digital pathology.
• The method streamlines annotation processes and can aid in training new pathologists by highlighting key diagnostic areas.