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Negative Instance Guided Self-Distillation Framework for Whole Slide Image Analysis.

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IEEE Journal of Biomedical and Health Informatics

|July 26, 2023

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This study introduces a novel negative instance-guided self-distillation framework for histopathology image classification. The method improves whole-slide image (WSI) analysis by directly training an instance-level classifier, outperforming existing approaches.

Area of Science:

Computational pathology
Medical image analysis
Deep learning

Background:

Histopathology image classification is crucial for clinical diagnosis.
Current deep learning methods for whole-slide images (WSIs) use multi-instance learning, often limiting performance by not fully exploring instance-level information.

Purpose of the Study:

To develop a novel framework for end-to-end training of an instance-level classifier for histopathology images.
To enhance the performance of both slide classification and positive patch localization compared to existing methods.

Main Methods:

Proposed a negative instance-guided, self-distillation framework for direct end-to-end instance-level classifier training.
Incorporated true negative instances to guide the student classifier in distinguishing positive and negative instances.

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Introduced a prediction bank to constrain pseudo instance label distribution and prevent model degeneration.

Main Results:

The proposed method significantly outperformed existing approaches on multiple public datasets (CAMELYON16, PANDA, TCGA) and an in-house dataset.
Demonstrated improved performance in both slide classification and positive patch localization.
The framework effectively guides the classifier and prevents self-distillation degeneration.

Conclusions:

The negative instance-guided, self-distillation framework offers a superior approach for histopathology image classification.
Directly training instance-level classifiers with guided distillation enhances diagnostic accuracy.
The method shows promise for clinical applications, including cervical cancer lymph node metastasis prediction.