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QuPaS: SAM-Based Semi-Supervised Histopathological Image Segmentation With Quantum Force Field Finetuning and

Siyang Feng, Xipeng Pan, Weidong Zhang

IEEE Transactions on Medical Imaging

|February 27, 2026

Summary

This summary is machine-generated.

Semi-supervised segmentation (S³), enhanced by the Segment Anything Model (SAM), improves histopathological image analysis. Our QuPaS framework leverages Quantum Force Field Finetuning and Adversarial Estimation for superior segmentation performance.

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Area of Science:

Digital Pathology
Medical Image Analysis
Computational Biology

Background:

Semi-supervised segmentation (S³) is crucial for histopathological image analysis.
Improving S³ models' learning from unlabeled data is a key challenge.
The Segment Anything Model (SAM) shows promise but struggles with complex histopathological image structures.

Purpose of the Study:

To develop an improved SAM-based S³ framework for histopathological image segmentation.
To address SAM's limitations in capturing fine structural relationships in complex images.
To enhance model learning capability using unlabeled data.

Main Methods:

Proposed QuPaS framework integrating Quantum Force Field (QFF) Finetuning and Adversarial Estimation (AE).
QFF simulates intermolecular forces to refine pixel-level feature understanding of spatial structures.
AE aligns confidence distributions across outputs to reduce semantic feature interference.

Main Results:

QuPaS significantly outperformed state-of-the-art S³ methods across three challenging histopathological segmentation tasks.
The framework demonstrated stable generalization on unseen domains.
Experimental validation confirmed QuPaS's effectiveness in complex segmentation scenarios.

Conclusions:

The QuPaS framework effectively enhances semi-supervised segmentation for histopathological images.
Integrating QFF and AE addresses SAM's limitations, improving structural relationship capture.
QuPaS offers a robust and generalizable solution for medical image segmentation.