1 code implementation • 1 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.
no code implementations • 16 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.
no code implementations • 4 Apr 2023 • Norman P. Jouppi, George Kurian, Sheng Li, Peter Ma, Rahul Nagarajan, Lifeng Nai, Nishant Patil, Suvinay Subramanian, Andy Swing, Brian Towles, Cliff Young, Xiang Zhou, Zongwei Zhou, David Patterson
For similar sized systems, it is ~4. 3x-4. 5x faster than the Graphcore IPU Bow and is 1. 2x-1. 7x faster and uses 1. 3x-1. 9x less power than the Nvidia A100.
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.
Ranked #1 on Tumor Segmentation on LiTS17
1 code implementation • 2 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.
Ranked #1 on Organ Segmentation on BTCV
no code implementations • CVPR 2023 • Wei Ji, Jingjing Li, Cheng Bian, Zongwei Zhou, Jiaying Zhao, Alan L. Yuille, Li Cheng
This gives rise to significantly more robust segmentation of image objects in complex scenes and under adverse conditions.
1 code implementation • 26 Oct 2022 • Qixin Hu, Junfei Xiao, Yixiong Chen, Shuwen Sun, Jie-Neng Chen, Alan Yuille, Zongwei Zhou
We develop a novel strategy to generate synthetic tumors.
1 code implementation • 23 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.
1 code implementation • 5 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.
1 code implementation • 6 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.
1 code implementation • 3 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.
5 code implementations • Google Research 2022 • Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, Parker Schuh, Kensen Shi, Sasha Tsvyashchenko, Joshua Maynez, Abhishek Rao, Parker Barnes, Yi Tay, Noam Shazeer, Vinodkumar Prabhakaran, Emily Reif, Nan Du, Ben Hutchinson, Reiner Pope, James Bradbury, Jacob Austin, Michael Isard, Guy Gur-Ari, Pengcheng Yin, Toju Duke, Anselm Levskaya, Sanjay Ghemawat, Sunipa Dev, Henryk Michalewski, Xavier Garcia, Vedant Misra, Kevin Robinson, Liam Fedus, Denny Zhou, Daphne Ippolito, David Luan, Hyeontaek Lim, Barret Zoph, Alexander Spiridonov, Ryan Sepassi, David Dohan, Shivani Agrawal, Mark Omernick, Andrew M. Dai, Thanumalayan Sankaranarayana Pillai, Marie Pellat, Aitor Lewkowycz, Erica Moreira, Rewon Child, Oleksandr Polozov, Katherine Lee, Zongwei Zhou, Xuezhi Wang, Brennan Saeta, Mark Diaz, Orhan Firat, Michele Catasta, Jason Wei, Kathy Meier-Hellstern, Douglas Eck, Jeff Dean, Slav Petrov, Noah Fiedel
To further our understanding of the impact of scale on few-shot learning, we trained a 540-billion parameter, densely activated, Transformer language model, which we call Pathways Language Model PaLM.
Ranked #1 on Common Sense Reasoning on BIG-bench (Known Unknowns)
no code implementations • 21 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.
no code implementations • 13 Dec 2021 • Nan Du, Yanping Huang, Andrew M. Dai, Simon Tong, Dmitry Lepikhin, Yuanzhong Xu, Maxim Krikun, Yanqi Zhou, Adams Wei Yu, Orhan Firat, Barret Zoph, Liam Fedus, Maarten Bosma, Zongwei Zhou, Tao Wang, Yu Emma Wang, Kellie Webster, Marie Pellat, Kevin Robinson, Kathleen Meier-Hellstern, Toju Duke, Lucas Dixon, Kun Zhang, Quoc V Le, Yonghui Wu, Zhifeng Chen, Claire Cui
Scaling language models with more data, compute and parameters has driven significant progress in natural language processing.
Ranked #8 on Question Answering on TriviaQA
1 code implementation • 3 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.
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.
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).
no code implementations • 27 Sep 2021 • Yu Zhang, Daniel S. Park, Wei Han, James Qin, Anmol Gulati, Joel Shor, Aren Jansen, Yuanzhong Xu, Yanping Huang, Shibo Wang, Zongwei Zhou, Bo Li, Min Ma, William Chan, Jiahui Yu, Yongqiang Wang, Liangliang Cao, Khe Chai Sim, Bhuvana Ramabhadran, Tara N. Sainath, Françoise Beaufays, Zhifeng Chen, Quoc V. Le, Chung-Cheng Chiu, Ruoming Pang, Yonghui Wu
We summarize the results of a host of efforts using giant automatic speech recognition (ASR) models pre-trained using large, diverse unlabeled datasets containing approximately a million hours of audio.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
2 code implementations • 25 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.
2 code implementations • 15 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.
1 code implementation • 29 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.
4 code implementations • 21 Feb 2021 • Fatemeh Haghighi, Mohammad Reza Hosseinzadeh Taher, Zongwei Zhou, Michael B. Gotway, Jianming Liang
This paper introduces a new concept called "transferable visual words" (TransVW), aiming to achieve annotation efficiency for deep learning in medical image analysis.
2 code implementations • 14 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.
Ranked #1 on Lung Nodule Detection on LUNA2016 FPRED
1 code implementation • 9 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.
11 code implementations • 11 Dec 2019 • Zongwei Zhou, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh, Jianming Liang
The state-of-the-art models for medical image segmentation are variants of U-Net and fully convolutional networks (FCN).
Ranked #1 on Medical Image Segmentation on EM (IoU metric)
no code implementations • 21 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.
2 code implementations • 19 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.
Ranked #1 on Pulmonary Embolism Detection on PE-CAD FPRED
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.
27 code implementations • 18 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
Ranked #1 on Video Polyp Segmentation on SUN-SEG-Easy
1 code implementation • 3 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.
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.
no code implementations • 7 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.