11 citations · 54 across the 21 of their papers we have counts for
21 papers
A Survey on Ordinal Regression: Applications, Advances and Prospects
Jinhong Wang, Jintai Chen, Jian Liu +3
Ordinal regression refers to classifying object instances into ordinal categories. Ordinal regression is crucial for applications in various areas like facial age estimation, image…
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…
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…
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…
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…
LKM-UNet: Large Kernel Vision Mamba UNet for Medical Image Segmentation
Jinhong Wang, Jintai Chen, Danny Chen +1
In clinical practice, medical image segmentation provides useful information on the contours and dimensions of target organs or tissues, facilitating improved diagnosis, analysis,…
AI-Enhanced Virtual Reality in Medicine: A Comprehensive Survey
Yixuan Wu, Kaiyuan Hu, Danny Z. Chen +1
With the rapid advance of computer graphics and artificial intelligence technologies, the ways we interact with the world have undergone a transformative shift. Virtual Reality (VR…
Text2Tree: Aligning Text Representation to the Label Tree Hierarchy for Imbalanced Medical Classification
Jiahuan Yan, Haojun Gao, Zhang Kai +4
Deep learning approaches exhibit promising performances on various text tasks. However, they are still struggling on medical text classification since samples are often extremely i…
OneSeg: Self-learning and One-shot Learning based Single-slice Annotation for 3D Medical Image Segmentation
Yixuan Wu, Bo Zheng, Jintai Chen +2
As deep learning methods continue to improve medical image segmentation performance, data annotation is still a big bottleneck due to the labor-intensive and time-consuming burden…
GCL: Gradient-Guided Contrastive Learning for Medical Image Segmentation with Multi-Perspective Meta Labels
Yixuan Wu, Jintai Chen, Jiahuan Yan +3
Since annotating medical images for segmentation tasks commonly incurs expensive costs, it is highly desirable to design an annotation-efficient method to alleviate the annotation…
Ord2Seq: Regarding Ordinal Regression as Label Sequence Prediction
Jinhong Wang, Yi Cheng, Jintai Chen +3
Ordinal regression refers to classifying object instances into ordinal categories. It has been widely studied in many scenarios, such as medical disease grading, movie rating, etc.…
Robust Image Ordinal Regression with Controllable Image Generation
Yi Cheng, Haochao Ying, Renjun Hu +5
Image ordinal regression has been mainly studied along the line of exploiting the order of categories. However, the issues of class imbalance and category overlap that are very com…
ExcelFormer: A neural network surpassing GBDTs on tabular data
Jintai Chen, Jiahuan Yan, Qiyuan Chen +3
Data organized in tabular format is ubiquitous in real-world applications, and users often craft tables with biased feature definitions and flexibly set prediction targets of their…
CTT-Net: A Multi-view Cross-token Transformer for Cataract Postoperative Visual Acuity Prediction
Jinhong Wang, Jingwen Wang, Tingting Chen +7
Surgery is the only viable treatment for cataract patients with visual acuity (VA) impairment. Clinically, to assess the necessity of cataract surgery, accurately predicting postop…
T2G-Former: Organizing Tabular Features into Relation Graphs Promotes Heterogeneous Feature Interaction
Jiahuan Yan, Jintai Chen, Yixuan Wu +2
Recent development of deep neural networks (DNNs) for tabular learning has largely benefited from the capability of DNNs for automatic feature interaction. However, the heterogenei…
Robust Training of Graph Neural Networks via Noise Governance
Siyi Qian, Haochao Ying, Renjun Hu +4
Graph Neural Networks (GNNs) have become widely-used models for semi-supervised learning. However, the robustness of GNNs in the presence of label noise remains a largely under-exp…
ME-GAN: Learning Panoptic Electrocardio Representations for Multi-view ECG Synthesis Conditioned on Heart Diseases
Jintai Chen, Kuanlun Liao, Kun Wei +3
Electrocardiogram (ECG) is a widely used non-invasive diagnostic tool for heart diseases. Many studies have devised ECG analysis models (e.g., classifiers) to assist diagnosis. As…
AGMI: Attention-Guided Multi-omics Integration for Drug Response Prediction with Graph Neural Networks
Ruiwei Feng, Yufeng Xie, Minshan Lai +3
Accurate drug response prediction (DRP) is a crucial yet challenging task in precision medicine. This paper presents a novel Attention-Guided Multi-omics Integration (AGMI) approac…