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Keio University

Academic institutionasia · jp
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Research library334linked papers
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Selected work

Representative Papers

Intuitive Surgical SurgToolLoc Challenge Results: 2022-2023

May 11, 2023

To address the challenge of real-time, robust surgical instrument localization in minimally invasive robotic-assisted surgery (RAS) video streams, this work introduces SurgToolLoc—the first large-scale, multi-view, multi-scenario benchmark dataset with pixel-level mask annotations. We further propose a novel evaluation protocol emphasizing both cross-center generalizability and real-time inference (≥30 FPS). Methodologically, we integrate instance segmentation and keypoint detection with temporal modeling (ConvLSTM/Transformer), domain adaptation, and weakly supervised learning. Our best-performing model achieves 92.4% mAP@0.5 on the test set while maintaining an inference speed of 36 FPS—substantially outperforming conventional template matching and early CNN-based approaches. The solution has undergone rigorous preclinical validation across multiple surgical scenarios. By providing a reproducible, scalable, end-to-end framework for visual instrument localization in RAS, this work establishes a new standard for benchmarking and advancing vision-based surgical navigation systems.

17 citationsRead paper

mmGAT: Pose Estimation by Graph Attention with Mutual Features from mmWave Radar Point Cloud

Jun 09, 2024ICC 2024 - IEEE International Conference on Communications

This work addresses the performance limitations of image-based human pose estimation in low-light, dark environments, and privacy-sensitive scenarios by proposing a robust and privacy-preserving solution based on millimeter-wave radar. By integrating Graph Attention Networks (GATs) with a cross-correlation feature extraction mechanism, the method achieves high-precision human pose modeling directly on radar point clouds for the first time. It effectively captures fine-grained spatial relationships inherent in human body structure. Evaluated on two public radar datasets, the approach establishes a new state-of-the-art, reducing Mean Per-Joint Position Error (MPJPE) by 35.6% and Protocol-Aware MPJPE (PA-MPJPE) by 14.1%, thereby significantly overcoming the environmental and privacy constraints that hinder conventional vision-based methods.

5 citations1 influentialRead paper

Abductive Reasoning with Syllogistic Forms in Large Language Models

Mar 06, 2026HAR

This study investigates whether large language models exhibit human-like cognitive biases in abductive reasoning, focusing on non-deductive inference tasks framed within syllogistic structures. To this end, the authors systematically reformulate traditional syllogisms into an abductive format—inferring the minor premise given the conclusion and major premise—and introduce a novel benchmark that integrates contextualized reasoning to evaluate both accuracy and plausibility. Experimental results reveal that current models remain susceptible to commonsense belief interference, leading to biased inferences and highlighting their limitations in complex abductive reasoning. This work not only presents an innovative framework for modeling and evaluating abductive reasoning in language models but also identifies key directions for advancing their human-like reasoning capabilities.

2 citations1 influentialRead paper
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