Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Videos

PAC-Net: patch adaptive cut-off network with differentiable module-wise K-learning for robust and efficient medical

Xiang Pan¹, Weiming Zhu², Herong Zheng³

¹Department of Computer Science and Technology, Zhejiang University of Technology, LiuHe Road 233, Hangzhou, Zhejiang, 310014, China.

Physics in Medicine and Biology

|June 29, 2026

Summary

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Generalizable 2D medical image segmentation via wavelet-guided spatial-frequency fusion network.

Biomedical physics & engineering express·2025

Same author

Bilateral network with text guided aggregation architecture for lung infection image segmentation.

Biomedical physics & engineering express·2025

Same journal

Four-dimensional on-beam computed tomography reconstruction using projection-difference images.

Physics in medicine and biology·2026

Same journal

Higher-order synergy-based ranking in transcriptomic communities via latent factors and O-information.

Physics in medicine and biology·2026

Same journal

Calculating biological dose distributions in hadrontherapy using GATE: the BioDose actor.

Physics in medicine and biology·2026

Same journal

A 1.5 mm BGO PET detector with DOI measurement.

Physics in medicine and biology·2026

Same journal

Development and validation of XrayMC: a dedicated Monte Carlo tool for X-ray imaging and radiation protection.

Physics in medicine and biology·2026

Same journal

A digital twin framework for adaptive treatment planning in radiotherapy.

Physics in medicine and biology·2026

See all related articles

This summary is machine-generated.

This study introduces PACNet, a novel medical image segmentation network with an adaptive attention mechanism. PACNet improves segmentation accuracy and robustness by learning data-dependent sparsity, outperforming existing methods.

Area of Science:

Medical Image Analysis
Computer Vision
Machine Learning

Background:

Sparse attention networks in medical image segmentation often use fixed K values, limiting adaptation to diverse lesion sizes and shapes.
This rigidity hinders robustness and segmentation accuracy in medical image analysis systems.

Purpose of the Study:

To develop an adaptive, differentiable sparse attention mechanism for medical image segmentation.
To enhance the robustness and segmentation accuracy of medical image analysis systems.

Main Methods:

Proposed PACNet (Patch Adaptive Cut-off Network) with an Entropy-Guided Differentiable K-Selection (EGDK) module.
EGDK learns data-dependent sparsity ratios using Gaussian Soft Indexing and a Straight-Through Estimator (STE) for end-to-end differentiability.

Keywords:

Attention based Medical Image Segmentation Entropy-Guided Differentiable K-Selection Gaussian Soft Indexing Patch Adaptive Cut-off Straight-Through Estimator

Related Experiment Videos

Evaluated on eight datasets across five imaging modalities.

Main Results:

PACNet achieved a superior average Dice Similarity Coefficient (DSC) of 90.57% across eight datasets.
Outperformed BRAU-Net++ by +1.79% in average DSC.
Demonstrated efficiency with 6.82M parameters and 6.02G FLOPs.

Conclusions:

Adaptive and differentiable K selection is superior to fixed or discrete methods for medical image segmentation.
PACNet effectively reduces background noise and preserves fine anatomical details.
Presents a practical and clinically relevant solution for robust medical image segmentation.