4k citations · 5.1k across the 42 of their papers we have counts for
42 papers
SwinMM: Masked Multi-view with Swin Transformers for 3D Medical Image Segmentation
Yiqing Wang, Zihan Li, Jieru Mei +7
Recent advancements in large-scale Vision Transformers have made significant strides in improving pre-trained models for medical image segmentation. However, these methods face a n…
Consistency-guided Meta-Learning for Bootstrapping Semi-Supervised Medical Image Segmentation
Qingyue Wei, Lequan Yu, Xianhang Li +4
Medical imaging has witnessed remarkable progress but usually requires a large amount of high-quality annotated data which is time-consuming and costly to obtain. To alleviate this…
Image Registration of In Vivo Micro-Ultrasound and Ex Vivo Pseudo-Whole Mount Histopathology Images of the Prostate: A Proof-of-Concept Study
Muhammad Imran, Brianna Nguyen, Jake Pensa +8
Early diagnosis of prostate cancer significantly improves a patient's 5-year survival rate. Biopsy of small prostate cancers is improved with image-guided biopsy. MRI-ultrasound fu…
MicroSegNet: A Deep Learning Approach for Prostate Segmentation on Micro-Ultrasound Images
Hongxu Jiang, Muhammad Imran, Preethika Muralidharan +12
Micro-ultrasound (micro-US) is a novel 29-MHz ultrasound technique that provides 3-4 times higher resolution than traditional ultrasound, potentially enabling low-cost, accurate di…
BiomedGPT: A Generalist Vision-Language Foundation Model for Diverse Biomedical Tasks
Kai Zhang, Rong Zhou, Eashan Adhikarla +20
Traditional biomedical artificial intelligence (AI) models, designed for specific tasks or modalities, often exhibit limited flexibility in real-world deployment and struggle to ut…
Distribution Aligned Diffusion and Prototype-guided network for Unsupervised Domain Adaptive Segmentation
Haipeng Zhou, Lei Zhu, Yuyin Zhou
The Diffusion Probabilistic Model (DPM) has emerged as a highly effective generative model in the field of computer vision. Its intermediate latent vectors offer rich semantic info…
Unleashing the Power of Visual Prompting At the Pixel Level
Junyang Wu, Xianhang Li, Chen Wei +4
This paper presents a simple and effective visual prompting method for adapting pre-trained models to downstream recognition tasks. Our method includes two key designs. First, rath…
Multi-Granularity Cross-modal Alignment for Generalized Medical Visual Representation Learning
Fuying Wang, Yuyin Zhou, Shujun Wang +2
Learning medical visual representations directly from paired radiology reports has become an emerging topic in representation learning. However, existing medical image-text joint l…
Bag of Tricks for FGSM Adversarial Training
Zichao Li, Li Liu, Zeyu Wang +2
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. However, d…
Multiple Instance Neuroimage Transformer
Ayush Singla, Qingyu Zhao, Daniel K. Do +3
For the first time, we propose using a multiple instance learning based convolution-free transformer model, called Multiple Instance Neuroimage Transformer (MINiT), for the classif…
Masked Autoencoders Enable Efficient Knowledge Distillers
Yutong Bai, Zeyu Wang, Junfei Xiao +5
This paper studies the potential of distilling knowledge from pre-trained models, especially Masked Autoencoders. Our approach is simple: in addition to optimizing the pixel recons…
A Simple Data Mixing Prior for Improving Self-Supervised Learning
Sucheng Ren, Huiyu Wang, Zhengqi Gao +4
Data mixing (e.g., Mixup, Cutmix, ResizeMix) is an essential component for advancing recognition models. In this paper, we focus on studying its effectiveness in the self-supervise…
Can CNNs Be More Robust Than Transformers?
Zeyu Wang, Yutong Bai, Yuyin Zhou +1
The recent success of Vision Transformers is shaking the long dominance of Convolutional Neural Networks (CNNs) in image recognition for a decade. Specifically, in terms of robustn…
In Defense of Image Pre-Training for Spatiotemporal Recognition
Xianhang Li, Huiyu Wang, Chen Wei +4
Image pre-training, the current de-facto paradigm for a wide range of visual tasks, is generally less favored in the field of video recognition. By contrast, a common strategy is t…
L2B: Learning to Bootstrap Robust Models for Combating Label Noise
Yuyin Zhou, Xianhang Li, Fengze Liu +6
Deep neural networks have shown great success in representation learning. However, when learning with noisy labels (LNL), they can easily overfit and fail to generalize to new data…
External Attention Assisted Multi-Phase Splenic Vascular Injury Segmentation with Limited Data
Yuyin Zhou, David Dreizin, Yan Wang +3
The spleen is one of the most commonly injured solid organs in blunt abdominal trauma. The development of automatic segmentation systems from multi-phase CT for splenic vascular in…
Learning Inductive Attention Guidance for Partially Supervised Pancreatic Ductal Adenocarcinoma Prediction
Yan Wang, Peng Tang, Yuyin Zhou +3
Pancreatic ductal adenocarcinoma (PDAC) is the third most common cause of cancer death in the United States. Predicting tumors like PDACs (including both classification and segment…
CateNorm: Categorical Normalization for Robust Medical Image Segmentation
Junfei Xiao, Lequan Yu, Zongwei Zhou +4
Batch normalization (BN) uniformly shifts and scales the activations based on the statistics of a batch of images. However, the intensity distribution of the background pixels ofte…
TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation
Jieneng Chen, Yongyi Lu, Qihang Yu +6
Medical image segmentation is an essential prerequisite for developing healthcare systems, especially for disease diagnosis and treatment planning. On various medical image segment…
Can Temporal Information Help with Contrastive Self-Supervised Learning?
Yutong Bai, Haoqi Fan, Ishan Misra +6
Leveraging temporal information has been regarded as essential for developing video understanding models. However, how to properly incorporate temporal information into the recent…
Volumetric Medical Image Segmentation: A 3D Deep Coarse-to-fine Framework and Its Adversarial Examples
Yingwei Li, Zhuotun Zhu, Yuyin Zhou +4
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 th…
Domain Adaptive Relational Reasoning for 3D Multi-Organ Segmentation
Shuhao Fu, Yongyi Lu, Yan Wang +4
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 t…
Neural Architecture Search for Lightweight Non-Local Networks
Yingwei Li, Xiaojie Jin, Jieru Mei +7
Non-Local (NL) blocks have been widely studied in various vision tasks. However, it has been rarely explored to embed the NL blocks in mobile neural networks, mainly due to the fol…
CAKES: Channel-wise Automatic KErnel Shrinking for Efficient 3D Networks
Qihang Yu, Yingwei Li, Jieru Mei +2
3D Convolution Neural Networks (CNNs) have been widely applied to 3D scene understanding, such as video analysis and volumetric image recognition. However, 3D networks can easily l…