173 citations · 570 across the 51 of their papers we have counts for
31 papers · 1 filter
Radiomics-Informed Deep Learning for Classification of Atrial Fibrillation Sub-Types from Left-Atrium CT Volumes
Weihang Dai, Xiaomeng Li, Taihui Yu +3
Atrial Fibrillation (AF) is characterized by rapid, irregular heartbeats, and can lead to fatal complications such as heart failure. The disease is divided into two sub-types based…
FCA: Taming Long-tailed Federated Medical Image Classification by Classifier Anchoring
Jeffry Wicaksana, Zengqiang Yan, Kwang-Ting Cheng
Limited training data and severe class imbalance impose significant challenges to developing clinically robust deep learning models. Federated learning (FL) addresses the former by…
Rethinking Boundary Detection in Deep Learning Models for Medical Image Segmentation
Yi Lin, Dong Zhang, Xiao Fang +3
Medical image segmentation is a fundamental task in the community of medical image analysis. In this paper, a novel network architecture, referred to as Convolution, Transformer, a…
Compete to Win: Enhancing Pseudo Labels for Barely-supervised Medical Image Segmentation
Huimin Wu, Xiaomeng Li, Yiqun Lin +1
This study investigates barely-supervised medical image segmentation where only few labeled data, i.e., single-digit cases are available. We observe the key limitation of the exist…
Exploring Visual Prompts for Whole Slide Image Classification with Multiple Instance Learning
Yi Lin, Zhongchen Zhao, Zhengjie ZHU +3
Multiple instance learning (MIL) has emerged as a popular method for classifying histopathology whole slide images (WSIs). However, existing approaches typically rely on pre-traine…
Few Shot Medical Image Segmentation with Cross Attention Transformer
Yi Lin, Yufan Chen, Kwang-Ting Cheng +1
Medical image segmentation has made significant progress in recent years. Deep learning-based methods are recognized as data-hungry techniques, requiring large amounts of data with…
Semi-Supervised Deep Regression with Uncertainty Consistency and Variational Model Ensembling via Bayesian Neural Networks
Weihang Dai, Xiaomeng Li, Kwang-Ting Cheng
Deep regression is an important problem with numerous applications. These range from computer vision tasks such as age estimation from photographs, to medical tasks such as ejectio…
Oscillation-free Quantization for Low-bit Vision Transformers
Shih-Yang Liu, Zechun Liu, Kwang-Ting Cheng
Weight oscillation is an undesirable side effect of quantization-aware training, in which quantized weights frequently jump between two quantized levels, resulting in training inst…
Randomized Quantization: A Generic Augmentation for Data Agnostic Self-supervised Learning
Huimin Wu, Chenyang Lei, Xiao Sun +5
Self-supervised representation learning follows a paradigm of withholding some part of the data and tasking the network to predict it from the remaining part. Among many techniques…
Cyclical Self-Supervision for Semi-Supervised Ejection Fraction Prediction from Echocardiogram Videos
Weihang Dai, Xiaomeng Li, Xinpeng Ding +1
Left-ventricular ejection fraction (LVEF) is an important indicator of heart failure. Existing methods for LVEF estimation from video require large amounts of annotated data to ach…
Graph Reasoning Transformer for Image Parsing
Dong Zhang, Jinhui Tang, Kwang-Ting Cheng
Capturing the long-range dependencies has empirically proven to be effective on a wide range of computer vision tasks. The progressive advances on this topic have been made through…
Understanding the Tricks of Deep Learning in Medical Image Segmentation: Challenges and Future Directions
Dong Zhang, Yi Lin, Hao Chen +4
Over the past few years, the rapid development of deep learning technologies for computer vision has significantly improved the performance of medical image segmentation (MedISeg).…
The Lighter The Better: Rethinking Transformers in Medical Image Segmentation Through Adaptive Pruning
Xian Lin, Li Yu, Kwang-Ting Cheng +1
Vision transformers have recently set off a new wave in the field of medical image analysis due to their remarkable performance on various computer vision tasks. However, recent hy…
Dynamic Sub-Cluster-Aware Network for Few-Shot Skin Disease Classification
Shuhan LI, Xiaomeng Li, Xiaowei Xu +1
This paper addresses the problem of few-shot skin disease classification by introducing a novel approach called the Sub-Cluster-Aware Network (SCAN) that enhances accuracy in diagn…
BATFormer: Towards Boundary-Aware Lightweight Transformer for Efficient Medical Image Segmentation
Xian Lin, Li Yu, Kwang-Ting Cheng +1
Objective: Transformers, born to remedy the inadequate receptive fields of CNNs, have drawn explosive attention recently. However, the daunting computational complexity of global r…
FedIIC: Towards Robust Federated Learning for Class-Imbalanced Medical Image Classification
Nannan Wu, Li Yu, Xin Yang +2
Federated learning (FL), training deep models from decentralized data without privacy leakage, has shown great potential in medical image computing recently. However, considering t…
FedMix: Mixed Supervised Federated Learning for Medical Image Segmentation
Jeffry Wicaksana, Zengqiang Yan, Dong Zhang +4
The purpose of federated learning is to enable multiple clients to jointly train a machine learning model without sharing data. However, the existing methods for training an image…
Stereo Neural Vernier Caliper
Shichao Li, Zechun Liu, Zhiqiang Shen +1
We propose a new object-centric framework for learning-based stereo 3D object detection. Previous studies build scene-centric representations that do not consider the significant v…
Vision Transformer Slimming: Multi-Dimension Searching in Continuous Optimization Space
Arnav Chavan, Zhiqiang Shen, Zhuang Liu +3
This paper explores the feasibility of finding an optimal sub-model from a vision transformer and introduces a pure vision transformer slimming (ViT-Slim) framework. It can search…
Adaptive Contrast for Image Regression in Computer-Aided Disease Assessment
Weihang Dai, Xiaomeng Li, Wan Hang Keith Chiu +2
Image regression tasks for medical applications, such as bone mineral density (BMD) estimation and left-ventricular ejection fraction (LVEF) prediction, play an important role in c…
Nonuniform-to-Uniform Quantization: Towards Accurate Quantization via Generalized Straight-Through Estimation
Zechun Liu, Kwang-Ting Cheng, Dong Huang +2
The nonuniform quantization strategy for compressing neural networks usually achieves better performance than its counterpart, i.e., uniform strategy, due to its superior represent…
Joint stereo 3D object detection and implicit surface reconstruction
Shichao Li, Xijie Huang, Zechun Liu +1
We present a new learning-based framework S-3D-RCNN that can recover accurate object orientation in SO(3) and simultaneously predict implicit rigid shapes from stereo RGB images. F…
Exploring Feature Representation Learning for Semi-supervised Medical Image Segmentation
Huimin Wu, Xiaomeng Li, Kwang-Ting Cheng
This paper presents a simple yet effective two-stage framework for semi-supervised medical image segmentation. Unlike prior state-of-the-art semi-supervised segmentation methods th…
Partial Is Better Than All: Revisiting Fine-tuning Strategy for Few-shot Learning
Zhiqiang Shen, Zechun Liu, Jie Qin +2
The goal of few-shot learning is to learn a classifier that can recognize unseen classes from limited support data with labels. A common practice for this task is to train a model…