GenSelfDiff-HIS: Generative Self-Supervision Using Diffusion for Histopathological Image Segmentation

Area of Science:

• Digital Pathology
• Machine Learning
• Computer Vision

Background:

• Histopathological image segmentation is crucial for disease diagnosis but is labor-intensive and requires expert pathologists.
• Supervised machine learning models require large annotated datasets, which are often unavailable, posing a bottleneck for histopathological image analysis.
• Self-supervised learning (SSL) leverages abundant unannotated data by training models on pretext tasks.

Purpose of the Study:

• To propose a novel SSL approach for histopathological image segmentation using generative diffusion models.
• To address the data scarcity issue in training segmentation models for histopathology.
• To explore generative diffusion as an effective pretext task for histopathological image segmentation.

Main Methods:

• Developed an SSL framework utilizing generative diffusion models as a pretext task for histopathological image segmentation.
• Employed image-to-image translation capabilities inherent in diffusion models for segmentation.
• Utilized multi-loss function-based fine-tuning for the downstream segmentation task.

Main Results:

• The proposed SSL method demonstrates effectiveness in segmenting histopathological images.
• Validation performed on two public datasets and a new head and neck cancer dataset.
• Generative diffusion proved to be a suitable pretext task for histopathological image segmentation.

Conclusions:

• Self-supervised learning with generative diffusion models presents a viable alternative for histopathological image segmentation.
• This approach can significantly reduce the reliance on large annotated datasets.
• The method shows promise for improving efficiency and accuracy in digital pathology workflows.