Toward Source-Free Cross Tissues Histopathological Cell Segmentation via Target-Specific Finetuning

Area of Science:

• Digital Pathology
• Computational Biology
• Medical Image Analysis

Background:

• Accurate histopathological cell analysis is crucial for cancer diagnosis.
• Deep learning for cell segmentation faces challenges due to data limitations and annotation requirements.
• Model generalization across diverse histopathological image types remains a significant hurdle.

Purpose of the Study:

• To develop a novel framework for self-supervised domain adaptation in cell segmentation.
• To enable accurate cell segmentation on unlabeled histopathological datasets without source data access.
• To improve the generalization of deep learning models for computer-aided diagnosis in pathology.

Main Methods:

• A target-specific finetuning-based self-supervised domain adaptation framework was proposed.
• The method fine-tunes pre-trained models on unlabeled target datasets with minimal parameter tuning.
• Incorporated local and global constraint terms considering pathological cell morphology for enhanced predictions.

Main Results:

• The proposed framework demonstrated promising performance in self-supervised cell segmentation across three different histopathological tissue types.
• The method achieved reliable predictions by leveraging morphological properties through constraint terms.
• Successful validation on cross-tissue histopathological images highlights model generalization capabilities.

Conclusions:

• The developed framework effectively addresses the challenge of model generalization in histopathological cell segmentation.
• This approach facilitates the application of computer-aided diagnosis tools in clinical pathology without requiring access to original training data.
• The self-supervised domain adaptation method offers a viable solution for leveraging unlabeled clinical data.