Selected work

Representative Papers

SurgLAT: Surgical Latent Attention Tracking for Depth-Aware Robotic Laparoscope Control

Aug 07, 2026

This work addresses the challenge of autonomous laparoscope control by interpreting surgeons’ implicit and time-varying operational intent in dynamic surgical environments, rather than tracking static targets. To this end, the authors propose SurgLAT, a novel framework that models implicit surgical attention as a causally evolving latent state. SurgLAT integrates memory-guided spatial priors with a dynamic retrieval mechanism to robustly track regions of interest. The method employs a frozen DINOv2 encoder, a state-conditioned spatial token mixer, a selective causal latent memory module, and a probabilistic attention heatmap decoder, combined with redundancy-aware null-space control respecting remote center of motion (RCM) constraints to ensure smooth and stable endoscope motion. Experiments demonstrate that the system exhibits strong robustness to occlusions, rapid movements, and target switches on both real surgical videos and robotic platforms.

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HFS-TriNet: A Three-Branch Collaborative Feature Learning Network for Prostate Cancer Classification from TRUS Videos

Apr 24, 2026

This study addresses the challenges in prostate cancer classification from transrectal ultrasound (TRUS) videos, including information redundancy, high intra- and inter-class similarity, and low signal-to-noise ratio, which hinder feature discriminability and diagnostic accuracy. To overcome these limitations, the authors propose HFS-TriNet, a novel architecture that integrates three parallel branches—leveraging the medical Segment Anything Model (SAM), wavelet-transform convolutional residual (WTCR) blocks, and ResNet50—augmented with a heuristic frame selection (HFS) mechanism and a normalized attention module. This design enables efficient extraction of multi-scale features that jointly capture edge details, semantic consistency, and spatiotemporal dynamics. The proposed method substantially mitigates redundancy and noise interference while maintaining computational efficiency, leading to significantly improved accuracy and robustness in prostate cancer classification.

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Recent publications

Latest Papers

SurgLAT: Surgical Latent Attention Tracking for Depth-Aware Robotic Laparoscope Control

Aug 07, 2026

This work addresses the challenge of autonomous laparoscope control by interpreting surgeons’ implicit and time-varying operational intent in dynamic surgical environments, rather than tracking static targets. To this end, the authors propose SurgLAT, a novel framework that models implicit surgical attention as a causally evolving latent state. SurgLAT integrates memory-guided spatial priors with a dynamic retrieval mechanism to robustly track regions of interest. The method employs a frozen DINOv2 encoder, a state-conditioned spatial token mixer, a selective causal latent memory module, and a probabilistic attention heatmap decoder, combined with redundancy-aware null-space control respecting remote center of motion (RCM) constraints to ensure smooth and stable endoscope motion. Experiments demonstrate that the system exhibits strong robustness to occlusions, rapid movements, and target switches on both real surgical videos and robotic platforms.

0 citationsRead paper

HFS-TriNet: A Three-Branch Collaborative Feature Learning Network for Prostate Cancer Classification from TRUS Videos

Apr 24, 2026

This study addresses the challenges in prostate cancer classification from transrectal ultrasound (TRUS) videos, including information redundancy, high intra- and inter-class similarity, and low signal-to-noise ratio, which hinder feature discriminability and diagnostic accuracy. To overcome these limitations, the authors propose HFS-TriNet, a novel architecture that integrates three parallel branches—leveraging the medical Segment Anything Model (SAM), wavelet-transform convolutional residual (WTCR) blocks, and ResNet50—augmented with a heuristic frame selection (HFS) mechanism and a normalized attention module. This design enables efficient extraction of multi-scale features that jointly capture edge details, semantic consistency, and spatiotemporal dynamics. The proposed method substantially mitigates redundancy and noise interference while maintaining computational efficiency, leading to significantly improved accuracy and robustness in prostate cancer classification.

0 citationsRead paper