63 citations · 239 across the 76 of their papers we have counts for
28 papers · 1 filter
ProtCLIP: Function-Informed Protein Multi-Modal Learning
Hanjing Zhou, Mingze Yin, Wei Wu +5
Multi-modality pre-training paradigm that aligns protein sequences and biological descriptions has learned general protein representations and achieved promising performance in var…
Towards Clinical Practice in CT-Based Pulmonary Disease Screening: An Efficient and Reliable Framework
Qian Shao, Bang Du, Yixuan Wu +6
Deep learning models for pulmonary disease screening from Computed Tomography (CT) scans promise to alleviate the immense workload on radiologists. Still, their high computational…
SALM: Sequence-Structure Pre-trained Large Language Model for Comprehensive Antibody Representation Learning
Mingze Yin, Hanjing Zhou, Jialu Wu +8
Antibodies safeguard our health through their precise and potent binding to specific antigens, demonstrating promising therapeutic efficacy in the treatment of numerous diseases, i…
Medical Video Generation for Disease Progression Simulation
Xu Cao, Kaizhao Liang, Kuei-Da Liao +7
Modeling disease progression is crucial for improving the quality and efficacy of clinical diagnosis and prognosis, but it is often hindered by a lack of longitudinal medical image…
AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings
Yue Wang, Xu Cao, Yaojun Hu +7
Electrocardiogram (ECG), a non-invasive and affordable tool for cardiac monitoring, is highly sensitive in detecting acute heart attacks. However, due to the lengthy nature of ECG…
Scalable Autoregressive Monocular Depth Estimation
Jinhong Wang, Jian Liu, Dongqi Tang +5
This paper shows that the autoregressive model is an effective and scalable monocular depth estimator. Our idea is simple: We tackle the monocular depth estimation (MDE) task with…
Protein-Mamba: Biological Mamba Models for Protein Function Prediction
Bohao Xu, Yingzhou Lu, Yoshitaka Inoue +3
Protein function prediction is a pivotal task in drug discovery, significantly impacting the development of effective and safe therapeutics. Traditional machine learning models oft…
Quantum-inspired Reinforcement Learning for Synthesizable Drug Design
Dannong Wang, Jintai Chen, Yingzhou Lu +5
Synthesizable molecular design (also known as synthesizable molecular optimization) is a fundamental problem in drug discovery, and involves designing novel molecular structures to…
Quantum-machine-assisted Drug Discovery
Yidong Zhou, Jintai Chen, Jinglei Cheng +8
Drug discovery is lengthy and expensive, with traditional computer-aided design facing limits. This paper examines integrating quantum computing across the drug development cycle t…
Graph-guided Cross-composition Feature Disentanglement for Compositional Zero-shot Learning
Yuxia Geng, Runkai Zhu, Jiaoyan Chen +7
Disentanglement of visual features of primitives (i.e., attributes and objects) has shown exceptional results in Compositional Zero-shot Learning (CZSL). However, due to the featur…
TeleOR: Real-time Telemedicine System for Full-Scene Operating Room
Yixuan Wu, Kaiyuan Hu, Qian Shao +3
The advent of telemedicine represents a transformative development in leveraging technology to extend the reach of specialized medical expertise to remote surgeries, a field where…
Multi-Modal CLIP-Informed Protein Editing
Mingze Yin, Hanjing Zhou, Yiheng Zhu +8
Proteins govern most biological functions essential for life, but achieving controllable protein discovery and optimization remains challenging. Recently, machine learning-assisted…
TrialEnroll: Predicting Clinical Trial Enrollment Success with Deep & Cross Network and Large Language Models
Ling Yue, Sixue Xing, Jintai Chen +1
Clinical trials need to recruit a sufficient number of volunteer patients to demonstrate the statistical power of the treatment (e.g., a new drug) in curing a certain disease. Clin…
Team up GBDTs and DNNs: Advancing Efficient and Effective Tabular Prediction with Tree-hybrid MLPs
Jiahuan Yan, Jintai Chen, Qianxing Wang +2
Tabular datasets play a crucial role in various applications. Thus, developing efficient, effective, and widely compatible prediction algorithms for tabular data is important. Curr…
TrialBench: Multi-Modal Artificial Intelligence-Ready Clinical Trial Datasets
Jintai Chen, Yaojun Hu, Mingchen Cai +13
Clinical trials are pivotal for developing new medical treatments but typically carry risks such as patient mortality and enrollment failure that waste immense efforts spanning ove…
What is the Visual Cognition Gap between Humans and Multimodal LLMs?
Xu Cao, Yifan Shen, Bolin Lai +8
Recently, Multimodal Large Language Models (MLLMs) and Vision Language Models (VLMs) have shown great promise in language-guided perceptual tasks such as recognition, segmentation,…
Cross-Table Pretraining towards a Universal Function Space for Heterogeneous Tabular Data
Jintai Chen, Zhen Lin, Qiyuan Chen +1
Tabular data from different tables exhibit significant diversity due to varied definitions and types of features, as well as complex inter-feature and feature-target relationships.…
Personalized Heart Disease Detection via ECG Digital Twin Generation
Yaojun Hu, Jintai Chen, Lianting Hu +5
Heart diseases rank among the leading causes of global mortality, demonstrating a crucial need for early diagnosis and intervention. Most traditional electrocardiogram (ECG) based…
ClinicalAgent: Clinical Trial Multi-Agent System with Large Language Model-based Reasoning
Ling Yue, Sixue Xing, Jintai Chen +1
Large Language Models (LLMs) and multi-agent systems have shown impressive capabilities in natural language tasks but face challenges in clinical trial applications, primarily due…
TrialDura: Hierarchical Attention Transformer for Interpretable Clinical Trial Duration Prediction
Ling Yue, Jonathan Li, Sixue Xing +4
The clinical trial process, a critical phase in drug development, is essential for developing new treatments. The primary goal of interventional clinical trials is to evaluate the…
Group-On: Boosting One-Shot Segmentation with Supportive Query
Hanjing Zhou, Mingze Yin, Danny Chen +2
One-shot semantic segmentation aims to segment query images given only ONE annotated support image of the same class. This task is challenging because target objects in the support…
Multi-rater Prompting for Ambiguous Medical Image Segmentation
Jinhong Wang, Yi Cheng, Jintai Chen +3
Multi-rater annotations commonly occur when medical images are independently annotated by multiple experts (raters). In this paper, we tackle two challenges arisen in multi-rater a…
PoCo: A Self-Supervised Approach via Polar Transformation Based Progressive Contrastive Learning for Ophthalmic Disease Diagnosis
Jinhong Wang, Tingting Chen, Jintai Chen +5
Automatic ophthalmic disease diagnosis on fundus images is important in clinical practice. However, due to complex fundus textures and limited annotated data, developing an effecti…
Making Pre-trained Language Models Great on Tabular Prediction
Jiahuan Yan, Bo Zheng, Hongxia Xu +5
The transferability of deep neural networks (DNNs) has made significant progress in image and language processing. However, due to the heterogeneity among tables, such DNN bonus is…