Search Results for author: Chengwei Pan

Found 17 papers, 8 papers with code

REALM: RAG-Driven Enhancement of Multimodal Electronic Health Records Analysis via Large Language Models

no code implementations10 Feb 2024 Yinghao Zhu, Changyu Ren, Shiyun Xie, Shukai Liu, Hangyuan Ji, Zixiang Wang, Tao Sun, Long He, Zhoujun Li, Xi Zhu, Chengwei Pan

Leveraging clinical notes and multivariate time-series EHR, existing models often lack the medical context relevent to clinical tasks, prompting the incorporation of external knowledge, particularly from the knowledge graph (KG).

Language Modelling Large Language Model +1

Progressive Conservative Adaptation for Evolving Target Domains

no code implementations7 Feb 2024 Gangming Zhao, Chaoqi Chen, Wenhao He, Chengwei Pan, Chaowei Fang, Jinpeng Li, Xilin Chen, Yizhou Yu

Moreover, as adjusting to the most recent target domain can interfere with the features learned from previous target domains, we develop a conservative sparse attention mechanism.

Domain Adaptation Meta-Learning +1

Prompting Large Language Models for Zero-Shot Clinical Prediction with Structured Longitudinal Electronic Health Record Data

1 code implementation25 Jan 2024 Yinghao Zhu, Zixiang Wang, Junyi Gao, Yuning Tong, Jingkun An, Weibin Liao, Ewen M. Harrison, Liantao Ma, Chengwei Pan

The inherent complexity of structured longitudinal Electronic Health Records (EHR) data poses a significant challenge when integrated with Large Language Models (LLMs), which are traditionally tailored for natural language processing.

Decision Making In-Context Learning

Leveraging Frequency Domain Learning in 3D Vessel Segmentation

no code implementations11 Jan 2024 Xinyuan Wang, Chengwei Pan, Hongming Dai, Gangming Zhao, Jinpeng Li, Xiao Zhang, Yizhou Yu

In this study, we leverage Fourier domain learning as a substitute for multi-scale convolutional kernels in 3D hierarchical segmentation models, which can reduce computational expenses while preserving global receptive fields within the network.

Segmentation

PRISM: Leveraging Prototype Patient Representations with Feature-Missing-Aware Calibration for EHR Data Sparsity Mitigation

1 code implementation8 Sep 2023 Yinghao Zhu, Zixiang Wang, Long He, Shiyun Xie, Liantao Ma, Chengwei Pan

Electronic Health Record (EHR) data, while rich in information, often suffers from sparsity, posing significant challenges in predictive modeling.

Imputation

Graph Convolution Based Cross-Network Multi-Scale Feature Fusion for Deep Vessel Segmentation

no code implementations6 Jan 2023 Gangming Zhao, Kongming Liang, Chengwei Pan, Fandong Zhang, Xianpeng Wu, Xinyang Hu, Yizhou Yu

To tackle the challenges caused by the sparsity and anisotropy of vessels, a higher percentage of graph nodes are distributed in areas that potentially contain vessels while a higher percentage of edges follow the orientation of potential nearbyvessels.

Segmentation

Deep 3D Vessel Segmentation based on Cross Transformer Network

1 code implementation22 Aug 2022 Chengwei Pan, Baolian Qi, Gangming Zhao, Jiaheng Liu, Chaowei Fang, Dingwen Zhang, Jinpeng Li

In CTN, a transformer module is constructed in parallel to a U-Net to learn long-distance dependencies between different anatomical regions; and these dependencies are communicated to the U-Net at multiple stages to endow it with global awareness.

Computed Tomography (CT) Segmentation

Computer-aided Tuberculosis Diagnosis with Attribute Reasoning Assistance

1 code implementation1 Jul 2022 Chengwei Pan, Gangming Zhao, Junjie Fang, Baolian Qi, Jiaheng Liu, Chaowei Fang, Dingwen Zhang, Jinpeng Li, Yizhou Yu

Although deep learning algorithms have been intensively developed for computer-aided tuberculosis diagnosis (CTD), they mainly depend on carefully annotated datasets, leading to much time and resource consumption.

Attribute Relational Reasoning +1

GREN: Graph-Regularized Embedding Network for Weakly-Supervised Disease Localization in X-ray Images

1 code implementation14 Jul 2021 Baolian Qi, Gangming Zhao, Xin Wei, Changde Du, Chengwei Pan, Yizhou Yu, Jinpeng Li

To model the relationship, we propose the Graph Regularized Embedding Network (GREN), which leverages the intra-image and inter-image information to locate diseases on chest X-ray images.

Decision Making

Learning Hybrid Representations for Automatic 3D Vessel Centerline Extraction

no code implementations14 Dec 2020 Jiafa He, Chengwei Pan, Can Yang, Ming Zhang, Yang Wang, Xiaowei Zhou, Yizhou Yu

The main idea is to use CNNs to learn local appearances of vessels in image crops while using another point-cloud network to learn the global geometry of vessels in the entire image.

Representation Learning

Rethinking the Extraction and Interaction of Multi-Scale Features for Vessel Segmentation

no code implementations9 Oct 2020 Yicheng Wu, Chengwei Pan, Shuqi Wang, Ming Zhang, Yong Xia, Yizhou Yu

Analyzing the morphological attributes of blood vessels plays a critical role in the computer-aided diagnosis of many cardiovascular and ophthalmologic diseases.

Segmentation-based Method combined with Dynamic Programming for Brain Midline Delineation

no code implementations27 Feb 2020 Shen Wang, Kongming Liang, Chengwei Pan, Chuyang Ye, Xiuli Li, Feng Liu, Yizhou Yu, Yizhou Wang

The midline related pathological image features are crucial for evaluating the severity of brain compression caused by stroke or traumatic brain injury (TBI).

Decision Making

Globally Guided Progressive Fusion Network for 3D Pancreas Segmentation

no code implementations23 Nov 2019 Chaowei Fang, Guanbin Li, Chengwei Pan, Yiming Li, Yizhou Yu

Recently 3D volumetric organ segmentation attracts much research interest in medical image analysis due to its significance in computer aided diagnosis.

Organ Segmentation Pancreas Segmentation +1

AADS: Augmented Autonomous Driving Simulation using Data-driven Algorithms

1 code implementation23 Jan 2019 Wei Li, Chengwei Pan, Rong Zhang, Jiaping Ren, Yuexin Ma, Jin Fang, Feilong Yan, Qichuan Geng, Xinyu Huang, Huajun Gong, Weiwei Xu, Guoping Wang, Dinesh Manocha, Ruigang Yang

Our augmented approach combines the flexibility in a virtual environment (e. g., vehicle movements) with the richness of the real world to allow effective simulation of anywhere on earth.

Autonomous Driving

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