Search Results for author: Zongwei Zhou

Found 32 papers, 23 papers with code

Continual Learning for Abdominal Multi-Organ and Tumor Segmentation

1 code implementation1 Jun 2023 Yixiao Zhang, Xinyi Li, Huimiao Chen, Alan Yuille, Yaoyao Liu, Zongwei Zhou

The ability to dynamically extend a model to new data and classes is critical for multiple organ and tumor segmentation.

Continual Learning Organ Segmentation +1

Annotating 8,000 Abdominal CT Volumes for Multi-Organ Segmentation in Three Weeks

no code implementations16 May 2023 Chongyu Qu, Tiezheng Zhang, Hualin Qiao, Jie Liu, Yucheng Tang, Alan Yuille, Zongwei Zhou

The conventional annotation methods would take an experienced annotator up to 1, 600 weeks (or roughly 30. 8 years) to complete this task.

Organ Segmentation

Label-Free Liver Tumor Segmentation

1 code implementation CVPR 2023 Qixin Hu, Yixiong Chen, Junfei Xiao, Shuwen Sun, Jieneng Chen, Alan Yuille, Zongwei Zhou

We demonstrate that AI models can accurately segment liver tumors without the need for manual annotation by using synthetic tumors in CT scans.

Tumor Segmentation

CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection

1 code implementation2 Jan 2023 Jie Liu, Yixiao Zhang, Jie-Neng Chen, Junfei Xiao, Yongyi Lu, Bennett A. Landman, Yixuan Yuan, Alan Yuille, Yucheng Tang, Zongwei Zhou

The proposed model is developed from an assembly of 14 datasets, using a total of 3, 410 CT scans for training and then evaluated on 6, 162 external CT scans from 3 additional datasets.

Organ Segmentation Transfer Learning

Delving into Masked Autoencoders for Multi-Label Thorax Disease Classification

1 code implementation23 Oct 2022 Junfei Xiao, Yutong Bai, Alan Yuille, Zongwei Zhou

We hope that this study can direct future research on the application of Transformers to a larger variety of medical imaging tasks.

Transfer Learning

Making Your First Choice: To Address Cold Start Problem in Vision Active Learning

1 code implementation5 Oct 2022 Liangyu Chen, Yutong Bai, Siyu Huang, Yongyi Lu, Bihan Wen, Alan L. Yuille, Zongwei Zhou

However, we uncover a striking contradiction to this promise: active learning fails to select data as efficiently as random selection at the first few choices.

Active Learning Contrastive Learning

Unsupervised Domain Adaptation through Shape Modeling for Medical Image Segmentation

1 code implementation6 Jul 2022 Yuan YAO, Fengze Liu, Zongwei Zhou, Yan Wang, Wei Shen, Alan Yuille, Yongyi Lu

Previous methods proposed Variational Autoencoder (VAE) based models to learn the distribution of shape for a particular organ and used it to automatically evaluate the quality of a segmentation prediction by fitting it into the learned shape distribution.

Image Segmentation Pancreas Segmentation +2

MetaLR: Meta-tuning of Learning Rates for Transfer Learning in Medical Imaging

1 code implementation3 Jun 2022 Yixiong Chen, Li Liu, Jingxian Li, Hua Jiang, Chris Ding, Zongwei Zhou

In this work, we propose a meta-learning-based LR tuner, named MetaLR, to make different layers automatically co-adapt to downstream tasks based on their transferabilities across domains.

Meta-Learning Transfer Learning

AutoDistill: an End-to-End Framework to Explore and Distill Hardware-Efficient Language Models

no code implementations21 Jan 2022 Xiaofan Zhang, Zongwei Zhou, Deming Chen, Yu Emma Wang

By evaluating on SQuAD, a model found by AutoDistill achieves an 88. 4% F1 score with 22. 8M parameters, which reduces parameters by more than 62% while maintaining higher accuracy than DistillBERT, TinyBERT, and NAS-BERT.

Bayesian Optimization Knowledge Distillation +2

MT-TransUNet: Mediating Multi-Task Tokens in Transformers for Skin Lesion Segmentation and Classification

1 code implementation3 Dec 2021 Jingye Chen, Jieneng Chen, Zongwei Zhou, Bin Li, Alan Yuille, Yongyi Lu

However, these approaches formulated skin cancer diagnosis as a simple classification task, dismissing the potential benefit from lesion segmentation.

Classification Lesion Classification +2

SQUID: Deep Feature In-Painting for Unsupervised Anomaly Detection

1 code implementation CVPR 2023 Tiange Xiang, Yixiao Zhang, Yongyi Lu, Alan L. Yuille, Chaoyi Zhang, Weidong Cai, Zongwei Zhou

Radiography imaging protocols focus on particular body regions, therefore producing images of great similarity and yielding recurrent anatomical structures across patients.

Anatomy Unsupervised Anomaly Detection

Learning from Temporal Gradient for Semi-supervised Action Recognition

1 code implementation CVPR 2022 Junfei Xiao, Longlong Jing, Lin Zhang, Ju He, Qi She, Zongwei Zhou, Alan Yuille, Yingwei Li

Our method achieves the state-of-the-art performance on three video action recognition benchmarks (i. e., Kinetics-400, UCF-101, and HMDB-51) under several typical semi-supervised settings (i. e., different ratios of labeled data).

Action Recognition Temporal Action Localization

Label-Assemble: Leveraging Multiple Datasets with Partial Labels

2 code implementations25 Sep 2021 Mintong Kang, Bowen Li, Zengle Zhu, Yongyi Lu, Elliot K. Fishman, Alan L. Yuille, Zongwei Zhou

We discovered that learning from negative examples facilitates both computer-aided disease diagnosis and detection.

COVID-19 Diagnosis Specificity

Seeking an Optimal Approach for Computer-Aided Pulmonary Embolism Detection

2 code implementations15 Sep 2021 Nahid Ul Islam, Shiv Gehlot, Zongwei Zhou, Michael B Gotway, Jianming Liang

At the image level, we compare convolutional neural networks (CNNs) with vision transformers, and contrast self-supervised learning (SSL) with supervised learning, followed by an evaluation of transfer learning compared with training from scratch.

Multiple Instance Learning Pulmonary Embolism Detection +2

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 +1

Learning Semantics-enriched Representation via Self-discovery, Self-classification, and Self-restoration

2 code implementations14 Jul 2020 Fatemeh Haghighi, Mohammad Reza Hosseinzadeh Taher, Zongwei Zhou, Michael B. Gotway, Jianming Liang

To this end, we train deep models to learn semantically enriched visual representation by self-discovery, self-classification, and self-restoration of the anatomy underneath medical images, resulting in a semantics-enriched, general-purpose, pre-trained 3D model, named Semantic Genesis.

Anatomy Brain Tumor Segmentation +7

Models Genesis

1 code implementation9 Apr 2020 Zongwei Zhou, Vatsal Sodha, Jiaxuan Pang, Michael B. Gotway, Jianming Liang

Transfer learning from natural images to medical images has been established as one of the most practical paradigms in deep learning for medical image analysis.

Anatomy Self-Supervised Learning +1

Scale MLPerf-0.6 models on Google TPU-v3 Pods

no code implementations21 Sep 2019 Sameer Kumar, Victor Bitorff, Dehao Chen, Chiachen Chou, Blake Hechtman, HyoukJoong Lee, Naveen Kumar, Peter Mattson, Shibo Wang, Tao Wang, Yuanzhong Xu, Zongwei Zhou

The recent submission of Google TPU-v3 Pods to the industry wide MLPerf v0. 6 training benchmark demonstrates the scalability of a suite of industry relevant ML models.


Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis

2 code implementations19 Aug 2019 Zongwei Zhou, Vatsal Sodha, Md Mahfuzur Rahman Siddiquee, Ruibin Feng, Nima Tajbakhsh, Michael B. Gotway, Jianming Liang

More importantly, learning a model from scratch simply in 3D may not necessarily yield performance better than transfer learning from ImageNet in 2D, but our Models Genesis consistently top any 2D approaches including fine-tuning the models pre-trained from ImageNet as well as fine-tuning the 2D versions of our Models Genesis, confirming the importance of 3D anatomical information and significance of our Models Genesis for 3D medical imaging.

Anatomy Brain Tumor Segmentation +6

Learning Fixed Points in Generative Adversarial Networks: From Image-to-Image Translation to Disease Detection and Localization

1 code implementation ICCV 2019 Md Mahfuzur Rahman Siddiquee, Zongwei Zhou, Nima Tajbakhsh, Ruibin Feng, Michael B. Gotway, Yoshua Bengio, Jianming Liang

Qualitative and quantitative evaluations demonstrate that the proposed method outperforms the state of the art in multi-domain image-to-image translation and that it surpasses predominant weakly-supervised localization methods in both disease detection and localization.

Image-to-Image Translation Translation

UNet++: A Nested U-Net Architecture for Medical Image Segmentation

27 code implementations18 Jul 2018 Zongwei Zhou, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh, Jianming Liang

Implementation of different kinds of Unet Models for Image Segmentation - Unet , RCNN-Unet, Attention Unet, RCNN-Attention Unet, Nested Unet

Image Segmentation Semantic Segmentation +2

Active, Continual Fine Tuning of Convolutional Neural Networks for Reducing Annotation Efforts

1 code implementation3 Feb 2018 Zongwei Zhou, Jae Y. Shin, Suryakanth R. Gurudu, Michael B. Gotway, Jianming Liang

The splendid success of convolutional neural networks (CNNs) in computer vision is largely attributable to the availability of massive annotated datasets, such as ImageNet and Places.

Active Learning Transfer Learning

Fine-Tuning Convolutional Neural Networks for Biomedical Image Analysis: Actively and Incrementally

no code implementations CVPR 2017 Zongwei Zhou, Jae Shin, Lei Zhang, Suryakanth Gurudu, Michael Gotway, Jianming Liang

Intense interest in applying convolutional neural networks (CNNs) in biomedical image analysis is wide spread, but its success is impeded by the lack of large annotated datasets in biomedical imaging.

Active Learning Transfer Learning

Comparison of machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer from 18F-FDG PET/CT images

no code implementations7 Feb 2017 Hongkai Wang, Zongwei Zhou, Yingci Li, Zhonghua Chen, Peiou Lu, Wenzhi Wang, Wanyu Liu, Lijuan Yu

The present study shows that the performance of CNN is not significantly different from the best classical methods and human doctors for classifying mediastinal lymph node metastasis of NSCLC from PET/CT images.

Tumor Segmentation

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