Search Results for author: Sihan Wang

Found 13 papers, 3 papers with code

Learning Concept-Driven Logical Rules for Interpretable and Generalizable Medical Image Classification

1 code implementation20 May 2025 Yibo Gao, Hangqi Zhou, Zheyao Gao, Bomin Wang, Shangqi Gao, Sihan Wang, Xiahai Zhuang

CRL employs logical layers to capture concept correlations and extract clinically meaningful rules, thereby providing both local and global interpretability.

image-classification Image Classification +1

Empowering Medical Multi-Agents with Clinical Consultation Flow for Dynamic Diagnosis

no code implementations19 Mar 2025 Sihan Wang, Suiyang Jiang, Yibo Gao, Boming Wang, Shangqi Gao, Xiahai Zhuang

While these models excel in static tasks, they struggle with dynamic diagnosis, failing to manage multi-turn interactions and often making premature diagnostic decisions due to insufficient persistence in information collection. To address this, we propose a multi-agent framework inspired by consultation flow and reinforcement learning (RL) to simulate the entire consultation process, integrating multiple clinical information for effective diagnosis.

Decision Making Diagnostic +1

InDeed: Interpretable image deep decomposition with guaranteed generalizability

no code implementations2 Jan 2025 Sihan Wang, Shangqi Gao, Fuping Wu, Xiahai Zhuang

In this work, we introduce a novel framework for interpretable deep image decomposition, combining hierarchical Bayesian modeling and deep learning to create an architecture-modularized and model-generalizable deep neural network (DNN).

Image Denoising Test-time Adaptation +1

Prototyping and Experimental Results for Environment-Aware Millimeter Wave Beam Alignment via Channel Knowledge Map

no code implementations13 Mar 2024 Zhuoyin Dai, Di wu, Zhenjun Dong, Kun Li, Dingyang Ding, Sihan Wang, Yong Zeng

In this paper, to alleviate the large training overhead in millimeter wave (mmWave) beam alignment, an environment-aware and training-free beam alignment prototype is established based on a typical CKM, termed beam index map (BIM).

Aligning Multi-Sequence CMR Towards Fully Automated Myocardial Pathology Segmentation

no code implementations7 Feb 2023 Wangbin Ding, Lei LI, Junyi Qiu, Sihan Wang, Liqin Huang, Yinyin Chen, Shan Yang, Xiahai Zhuang

For instance, balanced steady-state free precession cine sequences present clear anatomical boundaries, while late gadolinium enhancement and T2-weighted CMR sequences visualize myocardial scar and edema of MI, respectively.

Image Registration

MyoPS-Net: Myocardial Pathology Segmentation with Flexible Combination of Multi-Sequence CMR Images

no code implementations6 Nov 2022 Junyi Qiu, Lei LI, Sihan Wang, Ke Zhang, Yinyin Chen, Shan Yang, Xiahai Zhuang

We therefore conducted extensive experiments to investigate the performance of the proposed method in dealing with such complex combinations of different CMR sequences.

Segmentation

Decoupling Predictions in Distributed Learning for Multi-Center Left Atrial MRI Segmentation

1 code implementation10 Jun 2022 Zheyao Gao, Lei LI, Fuping Wu, Sihan Wang, Xiahai Zhuang

In this work, we propose a new framework of distributed learning that bridges the gap between two groups, and improves the performance for both generic and local data.

Medical Image Analysis MRI segmentation

Self-Supervised Transfer Learning for Hand Mesh Recovery From Binocular Images

no code implementations ICCV 2021 Zheng Chen, Sihan Wang, Yi Sun, Xiaohong Ma

Traditional methods for RGB hand mesh recovery usually need to train a separate model for each dataset with the corresponding ground truth and are hardly adapted to new scenarios without the ground truth for supervision.

Transfer Learning

Anatomy Prior Based U-net for Pathology Segmentation with Attention

no code implementations17 Nov 2020 Yuncheng Zhou, Ke Zhang, Xinzhe Luo, Sihan Wang, Xiahai Zhuang

Pathological area segmentation in cardiac magnetic resonance (MR) images plays a vital role in the clinical diagnosis of cardiovascular diseases.

Anatomy Segmentation

Multi-Modality Pathology Segmentation Framework: Application to Cardiac Magnetic Resonance Images

1 code implementation13 Aug 2020 Zhen Zhang, Chenyu Liu, Wangbin Ding, Sihan Wang, Chenhao Pei, Mingjing Yang, Liqin Huang

The PRSN is designed to segment pathological region based on the result of ASSN, in which a fusion block based on channel attention is proposed to better aggregate multi-modality information from multi-modality CMR images.

Denoising Segmentation

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