Search Results for author: Yuyin Zhou

Found 60 papers, 35 papers with code

A Flexible 2.5D Medical Image Segmentation Approach with In-Slice and Cross-Slice Attention

1 code implementation30 Apr 2024 Amarjeet Kumar, Hongxu Jiang, Muhammad Imran, Cyndi Valdes, Gabriela Leon, Dahyun Kang, Parvathi Nataraj, Yuyin Zhou, Michael D. Weiss, Wei Shao

This module uses the cross-slice attention mechanism to effectively capture 3D spatial information by learning long-range dependencies between the center slice (for segmentation) and its neighboring slices.

Computational Efficiency Image Segmentation +3

RetinaRegNet: A Versatile Approach for Retinal Image Registration

1 code implementation24 Apr 2024 Vishal Balaji Sivaraman, Muhammad Imran, Qingyue Wei, Preethika Muralidharan, Michelle R. Tamplin, Isabella M . Grumbach, Randy H. Kardon, Jui-Kai Wang, Yuyin Zhou, Wei Shao

The model's effectiveness was demonstrated across three retinal image datasets: color fundus images, fluorescein angiography images, and laser speckle flowgraphy images.

Image Registration

HQ-Edit: A High-Quality Dataset for Instruction-based Image Editing

no code implementations15 Apr 2024 Mude Hui, Siwei Yang, Bingchen Zhao, Yichun Shi, Heng Wang, Peng Wang, Yuyin Zhou, Cihang Xie

This study introduces HQ-Edit, a high-quality instruction-based image editing dataset with around 200, 000 edits.


Unleashing the Potential of SAM for Medical Adaptation via Hierarchical Decoding

1 code implementation27 Mar 2024 Zhiheng Cheng, Qingyue Wei, Hongru Zhu, Yan Wang, Liangqiong Qu, Wei Shao, Yuyin Zhou

This paper introduces H-SAM: a prompt-free adaptation of SAM tailored for efficient fine-tuning of medical images via a two-stage hierarchical decoding procedure.

Decoder Image Segmentation +4

3D-TransUNet for Brain Metastases Segmentation in the BraTS2023 Challenge

1 code implementation23 Mar 2024 Siwei Yang, Xianhang Li, Jieru Mei, Jieneng Chen, Cihang Xie, Yuyin Zhou

We identify that the Decoder-only 3D-TransUNet model should offer enhanced efficacy in the segmentation of brain metastases, as indicated by our 5-fold cross-validation on the training set.

Brain Tumor Segmentation Decoder +2

MicroDiffusion: Implicit Representation-Guided Diffusion for 3D Reconstruction from Limited 2D Microscopy Projections

1 code implementation16 Mar 2024 Mude Hui, Zihao Wei, Hongru Zhu, Fei Xia, Yuyin Zhou

This strategy enriches the diffusion process with structured 3D information, enhancing detail and reducing noise in localized 2D images.

3D Reconstruction Denoising

A Semantic Space is Worth 256 Language Descriptions: Make Stronger Segmentation Models with Descriptive Properties

1 code implementation21 Dec 2023 Junfei Xiao, Ziqi Zhou, Wenxuan Li, Shiyi Lan, Jieru Mei, Zhiding Yu, Alan Yuille, Yuyin Zhou, Cihang Xie

Instead of relying solely on category-specific annotations, ProLab uses descriptive properties grounded in common sense knowledge for supervising segmentation models.

Common Sense Reasoning Descriptive +1

Sculpting Holistic 3D Representation in Contrastive Language-Image-3D Pre-training

1 code implementation3 Nov 2023 Yipeng Gao, Zeyu Wang, Wei-Shi Zheng, Cihang Xie, Yuyin Zhou

Contrastive learning has emerged as a promising paradigm for 3D open-world understanding, i. e., aligning point cloud representation to image and text embedding space individually.

 Ranked #1 on Zero-shot 3D classification on Objaverse LVIS (using extra training data)

Contrastive Learning Retrieval +3

3D TransUNet: Advancing Medical Image Segmentation through Vision Transformers

3 code implementations11 Oct 2023 Jieneng Chen, Jieru Mei, Xianhang Li, Yongyi Lu, Qihang Yu, Qingyue Wei, Xiangde Luo, Yutong Xie, Ehsan Adeli, Yan Wang, Matthew Lungren, Lei Xing, Le Lu, Alan Yuille, Yuyin Zhou

In this paper, we extend the 2D TransUNet architecture to a 3D network by building upon the state-of-the-art nnU-Net architecture, and fully exploring Transformers' potential in both the encoder and decoder design.

Decoder Image Segmentation +4

FedConv: Enhancing Convolutional Neural Networks for Handling Data Heterogeneity in Federated Learning

1 code implementation6 Oct 2023 Peiran Xu, Zeyu Wang, Jieru Mei, Liangqiong Qu, Alan Yuille, Cihang Xie, Yuyin Zhou

Federated learning (FL) is an emerging paradigm in machine learning, where a shared model is collaboratively learned using data from multiple devices to mitigate the risk of data leakage.

Federated Learning

Boosting Dermatoscopic Lesion Segmentation via Diffusion Models with Visual and Textual Prompts

no code implementations4 Oct 2023 Shiyi Du, Xiaosong Wang, Yongyi Lu, Yuyin Zhou, Shaoting Zhang, Alan Yuille, Kang Li, Zongwei Zhou

Image synthesis approaches, e. g., generative adversarial networks, have been popular as a form of data augmentation in medical image analysis tasks.

Data Augmentation Image Generation +2

SwinMM: Masked Multi-view with Swin Transformers for 3D Medical Image Segmentation

1 code implementation24 Jul 2023 YiQing Wang, Zihan Li, Jieru Mei, Zihao Wei, Li Liu, Chen Wang, Shengtian Sang, Alan Yuille, Cihang Xie, Yuyin Zhou

To address this limitation, we present Masked Multi-view with Swin Transformers (SwinMM), a novel multi-view pipeline for enabling accurate and data-efficient self-supervised medical image analysis.

Contrastive Learning Image Reconstruction +4

Consistency-guided Meta-Learning for Bootstrapping Semi-Supervised Medical Image Segmentation

1 code implementation21 Jul 2023 Qingyue Wei, Lequan Yu, Xianhang Li, Wei Shao, Cihang Xie, Lei Xing, Yuyin Zhou

Specifically, our approach first involves training a segmentation model on a small set of clean labeled images to generate initial labels for unlabeled data.

Image Segmentation Meta-Learning +4

MicroSegNet: A Deep Learning Approach for Prostate Segmentation on Micro-Ultrasound Images

1 code implementation31 May 2023 Hongxu Jiang, Muhammad Imran, Preethika Muralidharan, Anjali Patel, Jake Pensa, Muxuan Liang, Tarik Benidir, Joseph R. Grajo, Jason P. Joseph, Russell Terry, John Michael DiBianco, Li-Ming Su, Yuyin Zhou, Wayne G. Brisbane, Wei Shao

During the training process, MicroSegNet focuses more on regions that are hard to segment (hard regions), characterized by discrepancies between expert and non-expert annotations.


Distribution Aligned Diffusion and Prototype-guided network for Unsupervised Domain Adaptive Segmentation

1 code implementation22 Mar 2023 Haipeng Zhou, Lei Zhu, Yuyin Zhou

In order to explore its potential further, we have taken a step forward and considered a more complex scenario in the medical image domain, specifically, under an unsupervised adaptation condition.

Unleashing the Power of Visual Prompting At the Pixel Level

1 code implementation20 Dec 2022 Junyang Wu, Xianhang Li, Chen Wei, Huiyu Wang, Alan Yuille, Yuyin Zhou, Cihang Xie

This paper presents a simple and effective visual prompting method for adapting pre-trained models to downstream recognition tasks.

Visual Prompting

Multi-Granularity Cross-modal Alignment for Generalized Medical Visual Representation Learning

2 code implementations12 Oct 2022 Fuying Wang, Yuyin Zhou, Shujun Wang, Varut Vardhanabhuti, Lequan Yu

In this paper, we present a novel Multi-Granularity Cross-modal Alignment (MGCA) framework for generalized medical visual representation learning by harnessing the naturally exhibited semantic correspondences between medical image and radiology reports at three different levels, i. e., pathological region-level, instance-level, and disease-level.

Contrastive Learning Image Classification +4

Bag of Tricks for FGSM Adversarial Training

no code implementations6 Sep 2022 Zichao Li, Li Liu, Zeyu Wang, Yuyin Zhou, Cihang Xie

Adversarial training (AT) with samples generated by Fast Gradient Sign Method (FGSM), also known as FGSM-AT, is a computationally simple method to train robust networks.

Masked Autoencoders Enable Efficient Knowledge Distillers

1 code implementation CVPR 2023 Yutong Bai, Zeyu Wang, Junfei Xiao, Chen Wei, Huiyu Wang, Alan Yuille, Yuyin Zhou, Cihang Xie

For example, by distilling the knowledge from an MAE pre-trained ViT-L into a ViT-B, our method achieves 84. 0% ImageNet top-1 accuracy, outperforming the baseline of directly distilling a fine-tuned ViT-L by 1. 2%.

Knowledge Distillation

Multiple Instance Neuroimage Transformer

1 code implementation19 Aug 2022 Ayush Singla, Qingyu Zhao, Daniel K. Do, Yuyin Zhou, Kilian M. Pohl, Ehsan Adeli

As a proof-of-concept, we evaluate the efficacy of our model by training it to identify sex from T1w-MRIs of two public datasets: Adolescent Brain Cognitive Development (ABCD) and the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA).

Brain Morphometry Multiple Instance Learning

A Simple Data Mixing Prior for Improving Self-Supervised Learning

1 code implementation CVPR 2022 Sucheng Ren, Huiyu Wang, Zhengqi Gao, Shengfeng He, Alan Yuille, Yuyin Zhou, Cihang Xie

More notably, our SDMP is the first method that successfully leverages data mixing to improve (rather than hurt) the performance of Vision Transformers in the self-supervised setting.

Representation Learning Self-Supervised Learning

Can CNNs Be More Robust Than Transformers?

1 code implementation7 Jun 2022 Zeyu Wang, Yutong Bai, Yuyin Zhou, Cihang Xie

The recent success of Vision Transformers is shaking the long dominance of Convolutional Neural Networks (CNNs) in image recognition for a decade.

Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging

1 code implementation17 May 2022 Rui Yan, Liangqiong Qu, Qingyue Wei, Shih-Cheng Huang, Liyue Shen, Daniel Rubin, Lei Xing, Yuyin Zhou

The collection and curation of large-scale medical datasets from multiple institutions is essential for training accurate deep learning models, but privacy concerns often hinder data sharing.

Federated Learning Privacy Preserving +2

In Defense of Image Pre-Training for Spatiotemporal Recognition

1 code implementation3 May 2022 Xianhang Li, Huiyu Wang, Chen Wei, Jieru Mei, Alan Yuille, Yuyin Zhou, Cihang Xie

Inspired by this observation, we hypothesize that the key to effectively leveraging image pre-training lies in the decomposition of learning spatial and temporal features, and revisiting image pre-training as the appearance prior to initializing 3D kernels.

STS Video Recognition

CD$^2$-pFed: Cyclic Distillation-guided Channel Decoupling for Model Personalization in Federated Learning

no code implementations8 Apr 2022 Yiqing Shen, Yuyin Zhou, Lequan Yu

Federated learning (FL) is a distributed learning paradigm that enables multiple clients to collaboratively learn a shared global model.

Federated Learning

L2B: Learning to Bootstrap Robust Models for Combating Label Noise

1 code implementation9 Feb 2022 Yuyin Zhou, Xianhang Li, Fengze Liu, Qingyue Wei, Xuxi Chen, Lequan Yu, Cihang Xie, Matthew P. Lungren, Lei Xing

Extensive experiments demonstrate that our method effectively mitigates the challenges of noisy labels, often necessitating few to no validation samples, and is well generalized to other tasks such as image segmentation.

Ranked #8 on Image Classification on Clothing1M (using clean data) (using extra training data)

Image Segmentation Learning with noisy labels +3

CD2-pFed: Cyclic Distillation-Guided Channel Decoupling for Model Personalization in Federated Learning

no code implementations CVPR 2022 Yiqing Shen, Yuyin Zhou, Lequan Yu

Federated learning (FL) is a distributed learning paradigm that enables multiple clients to collaboratively learn a shared global model.

Federated Learning

RadFusion: Benchmarking Performance and Fairness for Multimodal Pulmonary Embolism Detection from CT and EHR

no code implementations23 Nov 2021 Yuyin Zhou, Shih-Cheng Huang, Jason Alan Fries, Alaa Youssef, Timothy J. Amrhein, Marcello Chang, Imon Banerjee, Daniel Rubin, Lei Xing, Nigam Shah, Matthew P. Lungren

Despite the routine use of electronic health record (EHR) data by radiologists to contextualize clinical history and inform image interpretation, the majority of deep learning architectures for medical imaging are unimodal, i. e., they only learn features from pixel-level information.

Benchmarking Computed Tomography (CT) +2

Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning

1 code implementation CVPR 2022 Liangqiong Qu, Yuyin Zhou, Paul Pu Liang, Yingda Xia, Feifei Wang, Ehsan Adeli, Li Fei-Fei, Daniel Rubin

Federated learning is an emerging research paradigm enabling collaborative training of machine learning models among different organizations while keeping data private at each institution.

Federated Learning

Learning Inductive Attention Guidance for Partially Supervised Pancreatic Ductal Adenocarcinoma Prediction

no code implementations31 May 2021 Yan Wang, Peng Tang, Yuyin Zhou, Wei Shen, Elliot K. Fishman, Alan L. Yuille

We instantiate both the global and the local classifiers by multiple instance learning (MIL), where the attention guidance, indicating roughly where the PDAC regions are, is the key to bridging them: For global MIL based normal/PDAC classification, attention serves as a weight for each instance (voxel) during MIL pooling, which eliminates the distraction from the background; For local MIL based semi-supervised PDAC segmentation, the attention guidance is inductive, which not only provides bag-level pseudo-labels to training data without per-voxel annotations for MIL training, but also acts as a proxy of an instance-level classifier.

Multiple Instance Learning Segmentation

CateNorm: Categorical Normalization for Robust Medical Image Segmentation

1 code implementation29 Mar 2021 Junfei Xiao, Lequan Yu, Zongwei Zhou, Yutong Bai, Lei Xing, Alan Yuille, Yuyin Zhou

We propose a new normalization strategy, named categorical normalization (CateNorm), to normalize the activations according to categorical statistics.

Image Segmentation Medical Image Segmentation +2

TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

21 code implementations8 Feb 2021 Jieneng Chen, Yongyi Lu, Qihang Yu, Xiangde Luo, Ehsan Adeli, Yan Wang, Le Lu, Alan L. Yuille, Yuyin Zhou

Medical image segmentation is an essential prerequisite for developing healthcare systems, especially for disease diagnosis and treatment planning.

Cardiac Segmentation Decoder +4

Volumetric Medical Image Segmentation: A 3D Deep Coarse-to-fine Framework and Its Adversarial Examples

no code implementations29 Oct 2020 Yingwei Li, Zhuotun Zhu, Yuyin Zhou, Yingda Xia, Wei Shen, Elliot K. Fishman, Alan L. Yuille

Although deep neural networks have been a dominant method for many 2D vision tasks, it is still challenging to apply them to 3D tasks, such as medical image segmentation, due to the limited amount of annotated 3D data and limited computational resources.

Image Segmentation Pancreas Segmentation +2

Domain Adaptive Relational Reasoning for 3D Multi-Organ Segmentation

no code implementations18 May 2020 Shuhao Fu, Yongyi Lu, Yan Wang, Yuyin Zhou, Wei Shen, Elliot Fishman, Alan Yuille

In this paper, we present a novel unsupervised domain adaptation (UDA) method, named Domain Adaptive Relational Reasoning (DARR), to generalize 3D multi-organ segmentation models to medical data collected from different scanners and/or protocols (domains).

Organ Segmentation Relational Reasoning +3

Neural Architecture Search for Lightweight Non-Local Networks

2 code implementations CVPR 2020 Yingwei Li, Xiaojie Jin, Jieru Mei, Xiaochen Lian, Linjie Yang, Cihang Xie, Qihang Yu, Yuyin Zhou, Song Bai, Alan Yuille

However, it has been rarely explored to embed the NL blocks in mobile neural networks, mainly due to the following challenges: 1) NL blocks generally have heavy computation cost which makes it difficult to be applied in applications where computational resources are limited, and 2) it is an open problem to discover an optimal configuration to embed NL blocks into mobile neural networks.

Image Classification Neural Architecture Search

CAKES: Channel-wise Automatic KErnel Shrinking for Efficient 3D Networks

1 code implementation28 Mar 2020 Qihang Yu, Yingwei Li, Jieru Mei, Yuyin Zhou, Alan L. Yuille

3D Convolution Neural Networks (CNNs) have been widely applied to 3D scene understanding, such as video analysis and volumetric image recognition.

3D Medical Imaging Segmentation Action Recognition +3

Deep Distance Transform for Tubular Structure Segmentation in CT Scans

no code implementations CVPR 2020 Yan Wang, Xu Wei, Fengze Liu, Jieneng Chen, Yuyin Zhou, Wei Shen, Elliot K. Fishman, Alan L. Yuille

Tubular structure segmentation in medical images, e. g., segmenting vessels in CT scans, serves as a vital step in the use of computers to aid in screening early stages of related diseases.


Hyper-Pairing Network for Multi-Phase Pancreatic Ductal Adenocarcinoma Segmentation

no code implementations3 Sep 2019 Yuyin Zhou, Yingwei Li, Zhishuai Zhang, Yan Wang, Angtian Wang, Elliot Fishman, Alan Yuille, Seyoun Park

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers with an overall five-year survival rate of 8%.

Multi-Scale Attentional Network for Multi-Focal Segmentation of Active Bleed after Pelvic Fractures

no code implementations23 Jun 2019 Yuyin Zhou, David Dreizin, Yingwei Li, Zhishuai Zhang, Yan Wang, Alan Yuille

Trauma is the worldwide leading cause of death and disability in those younger than 45 years, and pelvic fractures are a major source of morbidity and mortality.


Adversarial Metric Attack and Defense for Person Re-identification

1 code implementation30 Jan 2019 Song Bai, Yingwei Li, Yuyin Zhou, Qizhu Li, Philip H. S. Torr

However, our work observes the extreme vulnerability of existing distance metrics to adversarial examples, generated by simply adding human-imperceptible perturbations to person images.

Adversarial Attack Benchmarking +2

Learning Transferable Adversarial Examples via Ghost Networks

1 code implementation9 Dec 2018 Yingwei Li, Song Bai, Yuyin Zhou, Cihang Xie, Zhishuai Zhang, Alan Yuille

The critical principle of ghost networks is to apply feature-level perturbations to an existing model to potentially create a huge set of diverse models.

Adversarial Attack

Abdominal multi-organ segmentation with organ-attention networks and statistical fusion

no code implementations23 Apr 2018 Yan Wang, Yuyin Zhou, Wei Shen, Seyoun Park, Elliot K. Fishman, Alan L. Yuille

To address these challenges, we introduce a novel framework for multi-organ segmentation by using organ-attention networks with reverse connections (OAN-RCs) which are applied to 2D views, of the 3D CT volume, and output estimates which are combined by statistical fusion exploiting structural similarity.

Organ Segmentation

Training Multi-organ Segmentation Networks with Sample Selection by Relaxed Upper Confident Bound

no code implementations7 Apr 2018 Yan Wang, Yuyin Zhou, Peng Tang, Wei Shen, Elliot K. Fishman, Alan L. Yuille

Based on the fact that very hard samples might have annotation errors, we propose a new sample selection policy, named Relaxed Upper Confident Bound (RUCB).

Image Segmentation Medical Image Segmentation +3

Semi-Supervised Multi-Organ Segmentation via Deep Multi-Planar Co-Training

no code implementations7 Apr 2018 Yuyin Zhou, Yan Wang, Peng Tang, Song Bai, Wei Shen, Elliot K. Fishman, Alan L. Yuille

In multi-organ segmentation of abdominal CT scans, most existing fully supervised deep learning algorithms require lots of voxel-wise annotations, which are usually difficult, expensive, and slow to obtain.

Image Segmentation Organ Segmentation +2

Improving Transferability of Adversarial Examples with Input Diversity

2 code implementations CVPR 2019 Cihang Xie, Zhishuai Zhang, Yuyin Zhou, Song Bai, Jian-Yu Wang, Zhou Ren, Alan Yuille

We hope that our proposed attack strategy can serve as a strong benchmark baseline for evaluating the robustness of networks to adversaries and the effectiveness of different defense methods in the future.

Adversarial Attack Image Classification

Visual Concepts and Compositional Voting

no code implementations13 Nov 2017 Jianyu Wang, Zhishuai Zhang, Cihang Xie, Yuyin Zhou, Vittal Premachandran, Jun Zhu, Lingxi Xie, Alan Yuille

We use clustering algorithms to study the population activities of the features and extract a set of visual concepts which we show are visually tight and correspond to semantic parts of vehicles.

Clustering Semantic Part Detection

Recurrent Saliency Transformation Network: Incorporating Multi-Stage Visual Cues for Small Organ Segmentation

2 code implementations CVPR 2018 Qihang Yu, Lingxi Xie, Yan Wang, Yuyin Zhou, Elliot K. Fishman, Alan L. Yuille

The key innovation is a saliency transformation module, which repeatedly converts the segmentation probability map from the previous iteration as spatial weights and applies these weights to the current iteration.

Organ Segmentation Pancreas Segmentation +1

Deep Supervision for Pancreatic Cyst Segmentation in Abdominal CT Scans

no code implementations22 Jun 2017 Yuyin Zhou, Lingxi Xie, Elliot K. Fishman, Alan L. Yuille

Inspired by the high relevance between the location of a pancreas and its cystic region, we introduce extra deep supervision into the segmentation network, so that cyst segmentation can be improved with the help of relatively easier pancreas segmentation.

Pancreas Segmentation Segmentation

Adversarial Examples for Semantic Segmentation and Object Detection

2 code implementations ICCV 2017 Cihang Xie, Jian-Yu Wang, Zhishuai Zhang, Yuyin Zhou, Lingxi Xie, Alan Yuille

Our observation is that both segmentation and detection are based on classifying multiple targets on an image (e. g., the basic target is a pixel or a receptive field in segmentation, and an object proposal in detection), which inspires us to optimize a loss function over a set of pixels/proposals for generating adversarial perturbations.

Adversarial Attack Object +4

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