173 citations · 570 across the 51 of their papers we have counts for
51 papers
RAEM: Robust Autonomous Exploration for Multi-Floor Environments with a Quadruped Robot
Zikang Yuan, Yuan Ren, Yian Wang +10
In this paper, we propose RAEM, a robust autonomous exploration framework for quadruped robots operating in multi-floor environments. Most existing ground-robot exploration approac…
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…
FoodWise: Food Waste Reduction and Behavior Change on Campus with Data Visualization and Gamification
Yue Yu, Sophia Yi, Xi Nan +6
Food waste presents a substantial challenge with significant environmental and economic ramifications, and its severity on campus environments is of particular concern. In response…
Quantization Variation: A New Perspective on Training Transformers with Low-Bit Precision
Xijie Huang, Zhiqiang Shen, Pingcheng Dong +1
Despite the outstanding performance of transformers in both language and vision tasks, the expanding computation and model size have increased the demand for efficient deployment.…
Efficient and Robust Quantization-aware Training via Adaptive Coreset Selection
Xijie Huang, Zechun Liu, Shih-Yang Liu +1
Quantization-aware training (QAT) is a representative model compression method to reduce redundancy in weights and activations. However, most existing QAT methods require end-to-en…
FedNoRo: Towards Noise-Robust Federated Learning by Addressing Class Imbalance and Label Noise Heterogeneity
Nannan Wu, Li Yu, Xuefeng Jiang +2
Federated noisy label learning (FNLL) is emerging as a promising tool for privacy-preserving multi-source decentralized learning. Existing research, relying on the assumption of cl…
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…
Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions
Luyang Luo, Xi Wang, Yi Lin +7
Breast cancer has reached the highest incidence rate worldwide among all malignancies since 2020. Breast imaging plays a significant role in early diagnosis and intervention to imp…
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…
Boosting Convolution with Efficient MLP-Permutation for Volumetric Medical Image Segmentation
Yi Lin, Xiao Fang, Dong Zhang +2
Recently, the advent of vision Transformer (ViT) has brought substantial advancements in 3D dataset benchmarks, particularly in 3D volumetric medical image segmentation (Vol-MedSeg…
DyBit: Dynamic Bit-Precision Numbers for Efficient Quantized Neural Network Inference
Jiajun Zhou, Jiajun Wu, Yizhao Gao +7
To accelerate the inference of deep neural networks (DNNs), quantization with low-bitwidth numbers is actively researched. A prominent challenge is to quantize the DNN models into…
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…
SDQ: Stochastic Differentiable Quantization with Mixed Precision
Xijie Huang, Zhiqiang Shen, Shichao Li +5
In order to deploy deep models in a computationally efficient manner, model quantization approaches have been frequently used. In addition, as new hardware that supports mixed bitw…
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…
InsMix: Towards Realistic Generative Data Augmentation for Nuclei Instance Segmentation
Yi Lin, Zeyu Wang, Kwang-Ting Cheng +1
Nuclei Segmentation from histology images is a fundamental task in digital pathology analysis. However, deep-learning-based nuclei segmentation methods often suffer from limited an…