Search Results for author: Hong-Yu Zhou

Found 41 papers, 21 papers with code

A Generalist Learner for Multifaceted Medical Image Interpretation

no code implementations13 May 2024 Hong-Yu Zhou, Subathra Adithan, Julián Nicolás Acosta, Eric J. Topol, Pranav Rajpurkar

MedVersa is the first to showcase the viability of multimodal generative medical AI in implementing multimodal outputs, inputs, and dynamic task specification, highlighting its potential as a multifunctional system for comprehensive medical image analysis.

Decision Making Language Modelling +1

SDR-Former: A Siamese Dual-Resolution Transformer for Liver Lesion Classification Using 3D Multi-Phase Imaging

no code implementations27 Feb 2024 Meng Lou, Hanning Ying, Xiaoqing Liu, Hong-Yu Zhou, Yuqing Zhang, Yizhou Yu

This study proposes a novel Siamese Dual-Resolution Transformer (SDR-Former) framework, specifically designed for liver lesion classification in 3D multi-phase CT and MR imaging with varying phase counts.

Computational Efficiency Lesion Classification

Swin-UMamba: Mamba-based UNet with ImageNet-based pretraining

1 code implementation5 Feb 2024 Jiarun Liu, Hao Yang, Hong-Yu Zhou, Yan Xi, Lequan Yu, Yizhou Yu, Yong Liang, Guangming Shi, Shaoting Zhang, Hairong Zheng, Shanshan Wang

However, it is challenging for existing methods to model long-range global information, where convolutional neural networks (CNNs) are constrained by their local receptive fields, and vision transformers (ViTs) suffer from high quadratic complexity of their attention mechanism.

Image Segmentation Medical Image Segmentation +1

Less Could Be Better: Parameter-efficient Fine-tuning Advances Medical Vision Foundation Models

1 code implementation22 Jan 2024 Chenyu Lian, Hong-Yu Zhou, Yizhou Yu, Liansheng Wang

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 transfer learning on computer vision tasks.

Transfer Learning

Multi-modal vision-language model for generalizable annotation-free pathological lesions localization and clinical diagnosis

1 code implementation4 Jan 2024 Hao Yang, Hong-Yu Zhou, Zhihuan Li, Yuanxu Gao, Cheng Li, Weijian Huang, Jiarun Liu, Hairong Zheng, Kang Zhang, Shanshan Wang

Defining pathologies automatically from medical images aids the understanding of the emergence and progression of diseases, and such an ability is crucial in clinical diagnostics.

Contrastive Learning Language Modelling

Multimodal self-supervised learning for lesion localization

no code implementations3 Jan 2024 Hao Yang, Hong-Yu Zhou, Cheng Li, Weijian Huang, Jiarun Liu, Yong Liang, Shanshan Wang

Multimodal deep learning utilizing imaging and diagnostic reports has made impressive progress in the field of medical imaging diagnostics, demonstrating a particularly strong capability for auxiliary diagnosis in cases where sufficient annotation information is lacking.

Contrastive Learning Multimodal Deep Learning +1

Enhancing Representation in Medical Vision-Language Foundation Models via Multi-Scale Information Extraction Techniques

no code implementations3 Jan 2024 Weijian Huang, Cheng Li, Hong-Yu Zhou, Jiarun Liu, Hao Yang, Yong Liang, Guangming Shi, Hairong Zheng, Shanshan Wang

The development of medical vision-language foundation models has attracted significant attention in the field of medicine and healthcare due to their promising prospect in various clinical applications.

Representation Learning

TransXNet: Learning Both Global and Local Dynamics with a Dual Dynamic Token Mixer for Visual Recognition

1 code implementation30 Oct 2023 Meng Lou, Hong-Yu Zhou, Sibei Yang, Yizhou Yu

Furthermore, when stacking token mixers that consist of convolution and self-attention to form a deep network, the static nature of convolution hinders the fusion of features previously generated by self-attention into convolution kernels.

Image Classification Object Detection +1

Activate and Reject: Towards Safe Domain Generalization under Category Shift

no code implementations ICCV 2023 Chaoqi Chen, Luyao Tang, Leitian Tao, Hong-Yu Zhou, Yue Huang, Xiaoguang Han, Yizhou Yu

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 domains and object classes often occur.

Domain Generalization Image Classification +3

A Transformer-based representation-learning model with unified processing of multimodal input for clinical diagnostics

1 code implementation1 Jun 2023 Hong-Yu Zhou, Yizhou Yu, Chengdi Wang, Shu Zhang, Yuanxu Gao, Jia Pan, Jun Shao, Guangming Lu, Kang Zhang, Weimin Li

During the diagnostic process, clinicians leverage multimodal information, such as chief complaints, medical images, and laboratory-test results.

Representation Learning

Advancing Radiograph Representation Learning with Masked Record Modeling

1 code implementation30 Jan 2023 Hong-Yu Zhou, Chenyu Lian, Liansheng Wang, Yizhou Yu

Modern studies in radiograph representation learning rely on either self-supervision to encode invariant semantics or associated radiology reports to incorporate medical expertise, while the complementarity between them is barely noticed.

Representation Learning

Protein Representation Learning via Knowledge Enhanced Primary Structure Modeling

1 code implementation30 Jan 2023 Hong-Yu Zhou, Yunxiang Fu, Zhicheng Zhang, Cheng Bian, Yizhou Yu

Protein representation learning has primarily benefited from the remarkable development of language models (LMs).

Representation Learning

GraVIS: Grouping Augmented Views from Independent Sources for Dermatology Analysis

no code implementations11 Jan 2023 Hong-Yu Zhou, Chixiang Lu, Liansheng Wang, Yizhou Yu

Self-supervised representation learning has been extremely successful in medical image analysis, as it requires no human annotations to provide transferable representations for downstream tasks.

Contrastive Learning Lesion Segmentation +3

PCRLv2: A Unified Visual Information Preservation Framework for Self-supervised Pre-training in Medical Image Analysis

1 code implementation2 Jan 2023 Hong-Yu Zhou, Chixiang Lu, Chaoqi Chen, Sibei Yang, Yizhou Yu

Recent advances in self-supervised learning (SSL) in computer vision are primarily comparative, whose goal is to preserve invariant and discriminative semantics in latent representations by comparing siamese image views.

Brain Tumor Segmentation Organ Segmentation +3

UNet-2022: Exploring Dynamics in Non-isomorphic Architecture

no code implementations27 Oct 2022 Jiansen Guo, Hong-Yu Zhou, Liansheng Wang, Yizhou Yu

These phenomena indicate the potential of UNet-2022 to become the model of choice for medical image segmentation.

Image Segmentation Lesion Segmentation +4

A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective

no code implementations27 Sep 2022 Chaoqi Chen, Yushuang Wu, Qiyuan Dai, Hong-Yu Zhou, Mutian Xu, Sibei Yang, Xiaoguang Han, Yizhou Yu

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 network analysis and recommender systems), computer vision (\emph{e. g.,} object detection and point cloud learning), and natural language processing (\emph{e. g.,} relation extraction and sequence learning), to name a few.

Graph Representation Learning object-detection +3

ProCo: Prototype-aware Contrastive Learning for Long-tailed Medical Image Classification

1 code implementation1 Sep 2022 Zhixiong Yang, Junwen Pan, Yanzhan Yang, Xiaozhou Shi, Hong-Yu Zhou, Zhicheng Zhang, Cheng Bian

The overall framework, namely as Prototype-aware Contrastive learning (ProCo), is unified as a single-stage pipeline in an end-to-end manner to alleviate the imbalanced problem in medical image classification, which is also a distinct progress than existing works as they follow the traditional two-stage pipeline.

Contrastive Learning Image Classification +1

Relation Matters: Foreground-aware Graph-based Relational Reasoning for Domain Adaptive Object Detection

no code implementations6 Jun 2022 Chaoqi Chen, Jiongcheng Li, Hong-Yu Zhou, Xiaoguang Han, Yue Huang, Xinghao Ding, Yizhou Yu

However, both the global and local alignment approaches fail to capture the topological relations among different foreground objects as the explicit dependencies and interactions between and within domains are neglected.

Domain Adaptation Graph Attention +5

Attribute Surrogates Learning and Spectral Tokens Pooling in Transformers for Few-shot Learning

1 code implementation CVPR 2022 Yangji He, Weihan Liang, Dongyang Zhao, Hong-Yu Zhou, Weifeng Ge, Yizhou Yu, Wenqiang Zhang

To improve data efficiency, we propose hierarchically cascaded transformers that exploit intrinsic image structures through spectral tokens pooling and optimize the learnable parameters through latent attribute surrogates.

Attribute Few-Shot Image Classification +2

Advancing 3D Medical Image Analysis with Variable Dimension Transform based Supervised 3D Pre-training

1 code implementation5 Jan 2022 Shu Zhang, Zihao Li, Hong-Yu Zhou, Jiechao Ma, Yizhou Yu

The difficulties in both data acquisition and annotation substantially restrict the sample sizes of training datasets for 3D medical imaging applications.

Contrastive Learning Medical Object Detection

Preservational Learning Improves Self-supervised Medical Image Models by Reconstructing Diverse Contexts

2 code implementations ICCV 2021 Hong-Yu Zhou, Chixiang Lu, Sibei Yang, Xiaoguang Han, Yizhou Yu

From this perspective, we introduce Preservational Learning to reconstruct diverse image contexts in order to preserve more information in learned representations.

Contrastive Learning Representation Learning +1

nnFormer: Interleaved Transformer for Volumetric Segmentation

2 code implementations7 Sep 2021 Hong-Yu Zhou, Jiansen Guo, Yinghao Zhang, Lequan Yu, Liansheng Wang, Yizhou Yu

Transformer, the model of choice for natural language processing, has drawn scant attention from the medical imaging community.

Image Segmentation Inductive Bias +3

ConvNets vs. Transformers: Whose Visual Representations are More Transferable?

no code implementations11 Aug 2021 Hong-Yu Zhou, Chixiang Lu, Sibei Yang, Yizhou Yu

Vision transformers have attracted much attention from computer vision researchers as they are not restricted to the spatial inductive bias of ConvNets.

Classification Depth Estimation +5

Bottom-Up Shift and Reasoning for Referring Image Segmentation

1 code implementation CVPR 2021 Sibei Yang, Meng Xia, Guanbin Li, Hong-Yu Zhou, Yizhou Yu

In this paper, we tackle the challenge by jointly performing compositional visual reasoning and accurate segmentation in a single stage via the proposed novel Bottom-Up Shift (BUS) and Bidirectional Attentive Refinement (BIAR) modules.

Image Segmentation Segmentation +2

Generalized Organ Segmentation by Imitating One-shot Reasoning using Anatomical Correlation

no code implementations30 Mar 2021 Hong-Yu Zhou, Hualuo Liu, Shilei Cao, Dong Wei, Chixiang Lu, Yizhou Yu, Kai Ma, Yefeng Zheng

In this paper, we show that such process can be integrated into the one-shot segmentation task which is a very challenging but meaningful topic.

One-Shot Segmentation Organ Segmentation +1

MixSearch: Searching for Domain Generalized Medical Image Segmentation Architectures

1 code implementation26 Feb 2021 Luyan Liu, Zhiwei Wen, Songwei Liu, Hong-Yu Zhou, Hongwei Zhu, Weicheng Xie, Linlin Shen, Kai Ma, Yefeng Zheng

Considering the scarcity of medical data, most datasets in medical image analysis are an order of magnitude smaller than those of natural images.

Decoder Image Segmentation +3

Difficulty-aware Glaucoma Classification with Multi-Rater Consensus Modeling

no code implementations29 Jul 2020 Shuang Yu, Hong-Yu Zhou, Kai Ma, Cheng Bian, Chunyan Chu, Hanruo Liu, Yefeng Zheng

However, when being used for model training, only the final ground-truth label is utilized, while the critical information contained in the raw multi-rater gradings regarding the image being an easy/hard case is discarded.

Classification General Classification +1

A Macro-Micro Weakly-supervised Framework for AS-OCT Tissue Segmentation

no code implementations20 Jul 2020 Munan Ning, Cheng Bian, Donghuan Lu, Hong-Yu Zhou, Shuang Yu, Chenglang Yuan, Yang Guo, Yaohua Wang, Kai Ma, Yefeng Zheng

Primary angle closure glaucoma (PACG) is the leading cause of irreversible blindness among Asian people.

Comparing to Learn: Surpassing ImageNet Pretraining on Radiographs By Comparing Image Representations

1 code implementation15 Jul 2020 Hong-Yu Zhou, Shuang Yu, Cheng Bian, Yifan Hu, Kai Ma, Yefeng Zheng

In deep learning era, pretrained models play an important role in medical image analysis, in which ImageNet pretraining has been widely adopted as the best way.

Learning to Discover Multi-Class Attentional Regions for Multi-Label Image Recognition

1 code implementation3 Jul 2020 Bin-Bin Gao, Hong-Yu Zhou

To bridge the gap between global and local streams, we propose a multi-class attentional region module which aims to make the number of attentional regions as small as possible and keep the diversity of these regions as high as possible.

Multi-Label Classification Multi-Label Image Classification

When Semi-Supervised Learning Meets Transfer Learning: Training Strategies, Models and Datasets

no code implementations13 Dec 2018 Hong-Yu Zhou, Avital Oliver, Jianxin Wu, Yefeng Zheng

While practitioners have had an intuitive understanding of these observations, we do a comprehensive emperical analysis and demonstrate that: (1) the gains from SSL techniques over a fully-supervised baseline are smaller when trained from a pre-trained model than when trained from random initialization, (2) when the domain of the source data used to train the pre-trained model differs significantly from the domain of the target task, the gains from SSL are significantly higher and (3) some SSL methods are able to advance fully-supervised baselines (like Pseudo-Label).

Pseudo Label Transfer Learning

Age Estimation Using Expectation of Label Distribution Learning

1 code implementation13 Jul 2018 Bin-Bin Gao, Hong-Yu Zhou, Jianxin Wu, Xin Geng

Age estimation performance has been greatly improved by using convolutional neural network.

Age Estimation Face Recognition +1

Vortex Pooling: Improving Context Representation in Semantic Segmentation

no code implementations17 Apr 2018 Chen-Wei Xie, Hong-Yu Zhou, Jianxin Wu

To be specific, our approach outperforms the previous state-of-the-art model named DeepLab v3 by 1. 5% on the PASCAL VOC 2012 val set and 0. 6% on the test set by replacing the Atrous Spatial Pyramid Pooling (ASPP) module in DeepLab v3 with the proposed Vortex Pooling.

Semantic Segmentation

Code Attention: Translating Code to Comments by Exploiting Domain Features

2 code implementations22 Sep 2017 Wenhao Zheng, Hong-Yu Zhou, Ming Li, Jianxin Wu

Appropriate comments of code snippets provide insight for code functionality, which are helpful for program comprehension.

Comment Generation

Sunrise or Sunset: Selective Comparison Learning for Subtle Attribute Recognition

no code implementations20 Jul 2017 Hong-Yu Zhou, Bin-Bin Gao, Jianxin Wu

The difficulty of image recognition has gradually increased from general category recognition to fine-grained recognition and to the recognition of some subtle attributes such as temperature and geolocation.


Adaptive Feeding: Achieving Fast and Accurate Detections by Adaptively Combining Object Detectors

no code implementations ICCV 2017 Hong-Yu Zhou, Bin-Bin Gao, Jianxin Wu

In this paper, we propose Adaptive Feeding (AF) to combine a fast (but less accurate) detector and an accurate (but slow) detector, by adaptively determining whether an image is easy or hard and choosing an appropriate detector for it.

object-detection Object Detection

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