262 citations · 710 across the 34 of their papers we have counts for
34 papers
Uncertainty Estimation of Large Language Models in Medical Question Answering
Jiaxin Wu, Yizhou Yu, Hong-Yu Zhou
Large Language Models (LLMs) show promise for natural language generation in healthcare, but risk hallucinating factually incorrect information. Deploying LLMs for medical question…
Cross-Dimensional Medical Self-Supervised Representation Learning Based on a Pseudo-3D Transformation
Fei Gao, Siwen Wang, Fandong Zhang +5
Medical image analysis suffers from a shortage of data, whether annotated or not. This becomes even more pronounced when it comes to 3D medical images. Self-Supervised Learning (SS…
SDR-Former: A Siamese Dual-Resolution Transformer for Liver Lesion Classification Using 3D Multi-Phase Imaging
Meng Lou, Hanning Ying, Xiaoqing Liu +3
Automated classification of liver lesions in multi-phase CT and MR scans is of clinical significance but challenging. This study proposes a novel Siamese Dual-Resolution Transforme…
Less Could Be Better: Parameter-efficient Fine-tuning Advances Medical Vision Foundation Models
Chenyu Lian, Hong-Yu Zhou, Yizhou Yu +1
Parameter-efficient fine-tuning (PEFT) that was initially developed for exploiting pre-trained large language models has recently emerged as an effective approach to perform transf…
Activate and Reject: Towards Safe Domain Generalization under Category Shift
Chaoqi Chen, Luyao Tang, Leitian Tao +4
Albeit the notable performance on in-domain test points, it is non-trivial for deep neural networks to attain satisfactory accuracy when deploying in the open world, where novel do…
TransXNet: Learning Both Global and Local Dynamics with a Dual Dynamic Token Mixer for Visual Recognition
Meng Lou, Shu Zhang, Hong-Yu Zhou +3
Recent studies have integrated convolutions into transformers to introduce inductive bias and improve generalization performance. However, the static nature of conventional convolu…
A Transformer-based representation-learning model with unified processing of multimodal input for clinical diagnostics
Hong-Yu Zhou, Yizhou Yu, Chengdi Wang +7
During the diagnostic process, clinicians leverage multimodal information, such as chief complaints, medical images, and laboratory-test results. Deep-learning models for aiding di…
Advancing Radiograph Representation Learning with Masked Record Modeling
Hong-Yu Zhou, Chenyu Lian, Liansheng Wang +1
Modern studies in radiograph representation learning rely on either self-supervision to encode invariant semantics or associated radiology reports to incorporate medical expertise,…
Protein Representation Learning via Knowledge Enhanced Primary Structure Modeling
Hong-Yu Zhou, Yunxiang Fu, Zhicheng Zhang +2
Protein representation learning has primarily benefited from the remarkable development of language models (LMs). Accordingly, pre-trained protein models also suffer from a problem…
GraVIS: Grouping Augmented Views from Independent Sources for Dermatology Analysis
Hong-Yu Zhou, Chixiang Lu, Liansheng Wang +1
Self-supervised representation learning has been extremely successful in medical image analysis, as it requires no human annotations to provide transferable representations for dow…
PCRLv2: A Unified Visual Information Preservation Framework for Self-supervised Pre-training in Medical Image Analysis
Hong-Yu Zhou, Chixiang Lu, Chaoqi Chen +2
Recent advances in self-supervised learning (SSL) in computer vision are primarily comparative, whose goal is to preserve invariant and discriminative semantics in latent represent…
UNet-2022: Exploring Dynamics in Non-isomorphic Architecture
Jiansen Guo, Hong-Yu Zhou, Liansheng Wang +1
Recent medical image segmentation models are mostly hybrid, which integrate self-attention and convolution layers into the non-isomorphic architecture. However, one potential drawb…
A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective
Chaoqi Chen, Yushuang Wu, Qiyuan Dai +5
Graph Neural Networks (GNNs) have gained momentum in graph representation learning and boosted the state of the art in a variety of areas, such as data mining (\emph{e.g.,} social…
ProCo: Prototype-aware Contrastive Learning for Long-tailed Medical Image Classification
Zhixiong Yang, Junwen Pan, Yanzhan Yang +4
Medical image classification has been widely adopted in medical image analysis. However, due to the difficulty of collecting and labeling data in the medical area, medical image da…
PieTrack: An MOT solution based on synthetic data training and self-supervised domain adaptation
Yirui Wang, Shenghua He, Youbao Tang +8
In order to cope with the increasing demand for labeling data and privacy issues with human detection, synthetic data has been used as a substitute and showing promising results in…
Relation Matters: Foreground-aware Graph-based Relational Reasoning for Domain Adaptive Object Detection
Chaoqi Chen, Jiongcheng Li, Hong-Yu Zhou +4
Domain Adaptive Object Detection (DAOD) focuses on improving the generalization ability of object detectors via knowledge transfer. Recent advances in DAOD strive to change the emp…
Attribute Surrogates Learning and Spectral Tokens Pooling in Transformers for Few-shot Learning
Yangji He, Weihan Liang, Dongyang Zhao +4
This paper presents new hierarchically cascaded transformers that can improve data efficiency through attribute surrogates learning and spectral tokens pooling. Vision transformers…
Advancing 3D Medical Image Analysis with Variable Dimension Transform based Supervised 3D Pre-training
Shu Zhang, Zihao Li, Hong-Yu Zhou +2
The difficulties in both data acquisition and annotation substantially restrict the sample sizes of training datasets for 3D medical imaging applications. As a result, constructing…
Preservational Learning Improves Self-supervised Medical Image Models by Reconstructing Diverse Contexts
Hong-Yu Zhou, Chixiang Lu, Sibei Yang +2
Preserving maximal information is one of principles of designing self-supervised learning methodologies. To reach this goal, contrastive learning adopts an implicit way which is co…
nnFormer: Interleaved Transformer for Volumetric Segmentation
Hong-Yu Zhou, Jiansen Guo, Yinghao Zhang +3
Transformer, the model of choice for natural language processing, has drawn scant attention from the medical imaging community. Given the ability to exploit long-term dependencies,…
ConvNets vs. Transformers: Whose Visual Representations are More Transferable?
Hong-Yu Zhou, Chixiang Lu, Sibei Yang +1
Vision transformers have attracted much attention from computer vision researchers as they are not restricted to the spatial inductive bias of ConvNets. However, although Transform…
SSMD: Semi-Supervised Medical Image Detection with Adaptive Consistency and Heterogeneous Perturbation
Hong-Yu Zhou, Chengdi Wang, Haofeng Li +4
Semi-Supervised classification and segmentation methods have been widely investigated in medical image analysis. Both approaches can improve the performance of fully-supervised met…
Generalized Organ Segmentation by Imitating One-shot Reasoning using Anatomical Correlation
Hong-Yu Zhou, Hualuo Liu, Shilei Cao +5
Learning by imitation is one of the most significant abilities of human beings and plays a vital role in human's computational neural system. In medical image analysis, given sever…
MixSearch: Searching for Domain Generalized Medical Image Segmentation Architectures
Luyan Liu, Zhiwei Wen, Songwei Liu +6
Considering the scarcity of medical data, most datasets in medical image analysis are an order of magnitude smaller than those of natural images. However, most Network Architecture…