no code implementations • 6 May 2024 • Joshua C. Zhao, Saurabh Bagchi, Salman Avestimehr, Kevin S. Chan, Somali Chaterji, Dimitris Dimitriadis, Jiacheng Li, Ninghui Li, Arash Nourian, Holger R. Roth
In this survey paper, we provide a comprehensive literature review of the different privacy attacks and defense methods in FL.
no code implementations • 12 Feb 2024 • Holger R. Roth, Ziyue Xu, Yuan-Ting Hsieh, Adithya Renduchintala, Isaac Yang, Zhihong Zhang, Yuhong Wen, Sean Yang, Kevin Lu, Kristopher Kersten, Camir Ricketts, Daguang Xu, Chester Chen, Yan Cheng, Andrew Feng
In the ever-evolving landscape of artificial intelligence (AI) and large language models (LLMs), handling and leveraging data effectively has become a critical challenge.
no code implementations • 2 Oct 2023 • Jingwei Sun, Ziyue Xu, Hongxu Yin, Dong Yang, Daguang Xu, Yiran Chen, Holger R. Roth
However, applying FL to finetune PLMs is hampered by challenges, including restricted model parameter access, high computational requirements, and communication overheads.
1 code implementation • 8 Aug 2023 • Pochuan Wang, Chen Shen, Weichung Wang, Masahiro Oda, Chiou-Shann Fuh, Kensaku MORI, Holger R. Roth
Federated learning (FL) is a key technology enabling the collaborative development of a model without exchanging training data.
1 code implementation • 18 May 2023 • Andres Diaz-Pinto, Pritesh Mehta, Sachidanand Alle, Muhammad Asad, Richard Brown, Vishwesh Nath, Alvin Ihsani, Michela Antonelli, Daniel Palkovics, Csaba Pinter, Ron Alkalay, Steve Pieper, Holger R. Roth, Daguang Xu, Prerna Dogra, Tom Vercauteren, Andrew Feng, Abood Quraini, Sebastien Ourselin, M. Jorge Cardoso
Automatic segmentation of medical images is a key step for diagnostic and interventional tasks.
no code implementations • ICCV 2023 • Jingwei Sun, Ziyue Xu, Dong Yang, Vishwesh Nath, Wenqi Li, Can Zhao, Daguang Xu, Yiran Chen, Holger R. Roth
We propose a practical vertical federated learning (VFL) framework called \textbf{one-shot VFL} that can solve the communication bottleneck and the problem of limited overlapping samples simultaneously based on semi-supervised learning.
1 code implementation • 4 Nov 2022 • M. Jorge Cardoso, Wenqi Li, Richard Brown, Nic Ma, Eric Kerfoot, Yiheng Wang, Benjamin Murrey, Can Zhao, Dong Yang, Vishwesh Nath, Yufan He, Ziyue Xu, Ali Hatamizadeh, Andriy Myronenko, Wentao Zhu, Yun Liu, Mingxin Zheng, Yucheng Tang, Isaac Yang, Michael Zephyr, Behrooz Hashemian, Sachidanand Alle, Mohammad Zalbagi Darestani, Charlie Budd, Marc Modat, Tom Vercauteren, Guotai Wang, Yiwen Li, Yipeng Hu, Yunguan Fu, Benjamin Gorman, Hans Johnson, Brad Genereaux, Barbaros S. Erdal, Vikash Gupta, Andres Diaz-Pinto, Andre Dourson, Lena Maier-Hein, Paul F. Jaeger, Michael Baumgartner, Jayashree Kalpathy-Cramer, Mona Flores, Justin Kirby, Lee A. D. Cooper, Holger R. Roth, Daguang Xu, David Bericat, Ralf Floca, S. Kevin Zhou, Haris Shuaib, Keyvan Farahani, Klaus H. Maier-Hein, Stephen Aylward, Prerna Dogra, Sebastien Ourselin, Andrew Feng
For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e. g. geometry, physiology, physics) of medical data being processed.
1 code implementation • 24 Oct 2022 • Holger R. Roth, Yan Cheng, Yuhong Wen, Isaac Yang, Ziyue Xu, Yuan-Ting Hsieh, Kristopher Kersten, Ahmed Harouni, Can Zhao, Kevin Lu, Zhihong Zhang, Wenqi Li, Andriy Myronenko, Dong Yang, Sean Yang, Nicola Rieke, Abood Quraini, Chester Chen, Daguang Xu, Nic Ma, Prerna Dogra, Mona Flores, Andrew Feng
Federated learning (FL) enables building robust and generalizable AI models by leveraging diverse datasets from multiple collaborators without centralizing the data.
no code implementations • 13 Sep 2022 • Vishwesh Nath, Dong Yang, Holger R. Roth, Daguang Xu
Which volume to annotate next is a challenging problem in building medical imaging datasets for deep learning.
no code implementations • 22 Aug 2022 • Holger R. Roth, Ali Hatamizadeh, Ziyue Xu, Can Zhao, Wenqi Li, Andriy Myronenko, Daguang Xu
Split learning (SL) has been proposed to train deep learning models in a decentralized manner.
2 code implementations • 23 Mar 2022 • Andres Diaz-Pinto, Sachidanand Alle, Vishwesh Nath, Yucheng Tang, Alvin Ihsani, Muhammad Asad, Fernando Pérez-García, Pritesh Mehta, Wenqi Li, Mona Flores, Holger R. Roth, Tom Vercauteren, Daguang Xu, Prerna Dogra, Sebastien Ourselin, Andrew Feng, M. Jorge Cardoso
MONAI Label allows researchers to make incremental improvements to their AI-based annotation application by making them available to other researchers and clinicians alike.
no code implementations • 12 Mar 2022 • Pengfei Guo, Dong Yang, Ali Hatamizadeh, An Xu, Ziyue Xu, Wenqi Li, Can Zhao, Daguang Xu, Stephanie Harmon, Evrim Turkbey, Baris Turkbey, Bradford Wood, Francesca Patella, Elvira Stellato, Gianpaolo Carrafiello, Vishal M. Patel, Holger R. Roth
Federated learning (FL) is a distributed machine learning technique that enables collaborative model training while avoiding explicit data sharing.
no code implementations • 14 Feb 2022 • Ali Hatamizadeh, Hongxu Yin, Pavlo Molchanov, Andriy Myronenko, Wenqi Li, Prerna Dogra, Andrew Feng, Mona G. Flores, Jan Kautz, Daguang Xu, Holger R. Roth
Federated learning (FL) allows the collaborative training of AI models without needing to share raw data.
no code implementations • ICCV 2021 • Dong Yang, Andriy Myronenko, Xiaosong Wang, Ziyue Xu, Holger R. Roth, Daguang Xu
Lesion segmentation in medical imaging has been an important topic in clinical research.
no code implementations • 19 Aug 2021 • Chen Shen, Pochuan Wang, Holger R. Roth, Dong Yang, Daguang Xu, Masahiro Oda, Weichung Wang, Chiou-Shann Fuh, Po-Ting Chen, Kao-Lang Liu, Wei-Chih Liao, Kensaku MORI
Federated learning (FL) for medical image segmentation becomes more challenging in multi-task settings where clients might have different categories of labels represented in their data.
no code implementations • 16 Jul 2021 • Holger R. Roth, Dong Yang, Wenqi Li, Andriy Myronenko, Wentao Zhu, Ziyue Xu, Xiaosong Wang, Daguang Xu
Building robust deep learning-based models requires diverse training data, ideally from several sources.
no code implementations • 7 Jan 2021 • Vishwesh Nath, Dong Yang, Bennett A. Landman, Daguang Xu, Holger R. Roth
The primary advantage being that active learning frameworks select data points that can accelerate the learning process of a model and can reduce the amount of data needed to achieve full accuracy as compared to a model trained on a randomly acquired data set.
no code implementations • 23 Nov 2020 • Dong Yang, Ziyue Xu, Wenqi Li, Andriy Myronenko, Holger R. Roth, Stephanie Harmon, Sheng Xu, Baris Turkbey, Evrim Turkbey, Xiaosong Wang, Wentao Zhu, Gianpaolo Carrafiello, Francesca Patella, Maurizio Cariati, Hirofumi Obinata, Hitoshi Mori, Kaku Tamura, Peng An, Bradford J. Wood, Daguang Xu
To facilitate CT analysis, recent efforts have focused on computer-aided characterization and diagnosis, which has shown promising results.
no code implementations • 28 Sep 2020 • Pochuan Wang, Chen Shen, Holger R. Roth, Dong Yang, Daguang Xu, Masahiro Oda, Kazunari Misawa, Po-Ting Chen, Kao-Lang Liu, Wei-Chih Liao, Wei-Chung Wang, Kensaku MORI
The performance of deep learning-based methods strongly relies on the number of datasets used for training.
2 code implementations • 25 Sep 2020 • Holger R. Roth, Dong Yang, Ziyue Xu, Xiaosong Wang, Daguang Xu
Here, we suggest using minimal user interaction in the form of extreme point clicks to train a segmentation model which, in effect, can be used to speed up medical image annotation.
no code implementations • 3 Sep 2020 • Holger R. Roth, Ken Chang, Praveer Singh, Nir Neumark, Wenqi Li, Vikash Gupta, Sharut Gupta, Liangqiong Qu, Alvin Ihsani, Bernardo C. Bizzo, Yuhong Wen, Varun Buch, Meesam Shah, Felipe Kitamura, Matheus Mendonça, Vitor Lavor, Ahmed Harouni, Colin Compas, Jesse Tetreault, Prerna Dogra, Yan Cheng, Selnur Erdal, Richard White, Behrooz Hashemian, Thomas Schultz, Miao Zhang, Adam McCarthy, B. Min Yun, Elshaimaa Sharaf, Katharina V. Hoebel, Jay B. Patel, Bryan Chen, Sean Ko, Evan Leibovitz, Etta D. Pisano, Laura Coombs, Daguang Xu, Keith J. Dreyer, Ittai Dayan, Ram C. Naidu, Mona Flores, Daniel Rubin, Jayashree Kalpathy-Cramer
Building robust deep learning-based models requires large quantities of diverse training data.
no code implementations • 20 Apr 2020 • Masahiro Oda, Holger R. Roth, Takayuki Kitasaka, Kazuhiro Furukawa, Ryoji Miyahara, Yoshiki Hirooka, Nassir Navab, Kensaku MORI
We utilize the shape estimation network (SEN), which estimates deformed colon shape during colonoscope insertions.
no code implementations • 20 Apr 2020 • Masahiro Oda, Holger R. Roth, Takayuki Kitasaka, Kazuhiro Furukawa, Ryoji Miyahara, Yoshiki Hirooka, Hidemi Goto, Nassir Navab, Kensaku MORI
We propose a colon deformation estimation method using RNN and obtain the colonoscope shape from electromagnetic sensors during its insertion into the colon.
no code implementations • 5 Aug 2019 • Chenglong Wang, Holger R. Roth, Takayuki Kitasaka, Masahiro Oda, Yuichiro Hayashi, Yasushi Yoshino, Tokunori Yamamoto, Naoto Sassa, Momokazu Goto, Kensaku MORI
Then we generate a Voronoi diagram to estimate the renal vascular dominant regions based on the segmented kidney and renal arteries.
no code implementations • 8 Jun 2018 • Masahiro Oda, Natsuki Shimizu, Holger R. Roth, Ken'ichi Karasawa, Takayuki Kitasaka, Kazunari Misawa, Michitaka Fujiwara, Daniel Rueckert, Kensaku MORI
We estimate the position and the size of the pancreas (localized) from global features by regression forests.
no code implementations • 6 Jun 2018 • Holger R. Roth, Chen Shen, Hirohisa ODA, Takaaki Sugino, Masahiro Oda, Yuichiro Hayashi, Kazunari Misawa, Kensaku MORI
Recent advances in deep learning, like 3D fully convolutional networks (FCNs), have improved the state-of-the-art in dense semantic segmentation of medical images.
no code implementations • 11 Apr 2018 • Takayasu Moriya, Holger R. Roth, Shota NAKAMURA, Hirohisa ODA, Kai Nagara, Masahiro Oda, Kensaku MORI
This paper presents a novel unsupervised segmentation method for 3D medical images.
no code implementations • 11 Apr 2018 • Takayasu Moriya, Holger R. Roth, Shota NAKAMURA, Hirohisa ODA, Kai Nagara, Masahiro Oda, Kensaku MORI
In this paper, we propose a unified approach to unsupervised representation learning and clustering for pathology image segmentation.
no code implementations • 23 Mar 2018 • Holger R. Roth, Chen Shen, Hirohisa ODA, Masahiro Oda, Yuichiro Hayashi, Kazunari Misawa, Kensaku MORI
However, recent advances in deep learning have made it possible to significantly improve the performance of image recognition and semantic segmentation methods in the field of computer vision.
1 code implementation • 14 Mar 2018 • Holger R. Roth, Hirohisa ODA, Xiangrong Zhou, Natsuki Shimizu, Ying Yang, Yuichiro Hayashi, Masahiro Oda, Michitaka Fujiwara, Kazunari Misawa, Kensaku MORI
In this work, we show that a multi-class 3D FCN trained on manually labeled CT scans of several anatomical structures (ranging from the large organs to thin vessels) can achieve competitive segmentation results, while avoiding the need for handcrafting features or training class-specific models.
Ranked #2 on 3D Medical Imaging Segmentation on TCIA Pancreas-CT
no code implementations • 18 Jan 2018 • Chen Shen, Holger R. Roth, Hirohisa ODA, Masahiro Oda, Yuichiro Hayashi, Kazunari Misawa, Kensaku MORI
Deep learning-based methods achieved impressive results for the segmentation of medical images.
1 code implementation • 21 Apr 2017 • Holger R. Roth, Hirohisa ODA, Yuichiro Hayashi, Masahiro Oda, Natsuki Shimizu, Michitaka Fujiwara, Kazunari Misawa, Kensaku MORI
In this work, we show that a multi-class 3D FCN trained on manually labeled CT scans of seven abdominal structures (artery, vein, liver, spleen, stomach, gallbladder, and pancreas) can achieve competitive segmentation results, while avoiding the need for handcrafting features or training organ-specific models.
no code implementations • 15 Mar 2017 • Mohammad Eshghi, Holger R. Roth, Masahiro Oda, Min Suk Chung, Kensaku MORI
This paper presents an end-to-end pixelwise fully automated segmentation of the head sectioned images of the Visible Korean Human (VKH) project based on Deep Convolutional Neural Networks (DCNNs).
no code implementations • 27 Feb 2017 • Holger R. Roth, Kai Nagara, Hirohisa ODA, Masahiro Oda, Tomoshi Sugiyama, Shota NAKAMURA, Kensaku MORI
The alignment of clinical CT with $\mu$CT will allow further registration with even finer resolutions of $\mu$CT (up to 10 $\mu$m resolution) and ultimately with histopathological microscopy images for further macro to micro image fusion that can aid medical image analysis.
no code implementations • 31 Jan 2017 • Holger R. Roth, Le Lu, Nathan Lay, Adam P. Harrison, Amal Farag, Andrew Sohn, Ronald M. Summers
Accurate and automatic organ segmentation from 3D radiological scans is an important yet challenging problem for medical image analysis.
Ranked #1 on 3D Medical Imaging Segmentation on TCIA Pancreas-CT
no code implementations • 24 Jun 2016 • Holger R. Roth, Le Lu, Amal Farag, Andrew Sohn, Ronald M. Summers
Accurate automatic organ segmentation is an important yet challenging problem for medical image analysis.
no code implementations • 10 Feb 2016 • Hoo-chang Shin, Holger R. Roth, Mingchen Gao, Le Lu, Ziyue Xu, Isabella Nogues, Jianhua Yao, Daniel Mollura, Ronald M. Summers
Another effective method is transfer learning, i. e., fine-tuning CNN models pre-trained from natural image dataset to medical image tasks.
no code implementations • 1 Feb 2016 • Yinong Wang, Jianhua Yao, Holger R. Roth, Joseph E. Burns, Ronald M. Summers
The use of joint vertebra-rib atlases produced a statistically significant increase in the Dice coefficient from 92. 5 $\pm$ 3. 1% to 93. 8 $\pm$ 2. 1% for the left and right transverse processes and a decrease in the mean and max surface distance from 0. 75 $\pm$ 0. 60mm and 8. 63 $\pm$ 4. 44mm to 0. 30 $\pm$ 0. 27mm and 3. 65 $\pm$ 2. 87mm, respectively.
no code implementations • 29 Jan 2016 • Holger R. Roth, Yinong Wang, Jianhua Yao, Le Lu, Joseph E. Burns, Ronald M. Summers
In this work, we apply deep convolutional networks (ConvNets) for the automated detection of posterior element fractures of the spine.
no code implementations • 13 Jan 2016 • Yinong Wang, Jianhua Yao, Holger R. Roth, Joseph E. Burns, Ronald M. Summers
The precise and accurate segmentation of the vertebral column is essential in the diagnosis and treatment of various orthopedic, neurological, and oncological traumas and pathologies.
no code implementations • 22 Jun 2015 • Holger R. Roth, Le Lu, Amal Farag, Hoo-chang Shin, Jiamin Liu, Evrim Turkbey, Ronald M. Summers
We propose and evaluate several variations of deep ConvNets in the context of hierarchical, coarse-to-fine classification on image patches and regions, i. e. superpixels.
no code implementations • 22 May 2015 • Amal Farag, Le Lu, Holger R. Roth, Jiamin Liu, Evrim Turkbey, Ronald M. Summers
We present a bottom-up approach for pancreas segmentation in abdominal CT scans that is based on a hierarchy of information propagation by classifying image patches at different resolutions; and cascading superpixels.
no code implementations • 12 May 2015 • Holger R. Roth, Le Lu, Jiamin Liu, Jianhua Yao, Ari Seff, Kevin Cherry, Lauren Kim, Ronald M. Summers
By leveraging existing CAD systems, coordinates of regions or volumes of interest (ROI or VOI) for lesion candidates are generated in this step and function as input for a second tier, which is our focus in this study.
1 code implementation • 15 Apr 2015 • Holger R. Roth, Christopher T. Lee, Hoo-chang Shin, Ari Seff, Lauren Kim, Jianhua Yao, Le Lu, Ronald M. Summers
We show that a data augmentation approach can help to enrich the data set and improve classification performance.
no code implementations • 15 Apr 2015 • Holger R. Roth, Amal Farag, Le Lu, Evrim B. Turkbey, Ronald M. Summers
We evaluate our method on CT images of 82 patients (60 for training, 2 for validation, and 20 for testing).
no code implementations • 15 Jan 2015 • Holger R. Roth, Thomas E. Hampshire, Emma Helbren, Mingxing Hu, Roser Vega, Steve Halligan, David J. Hawkes
Furthermore, we evaluate the matching of the reconstructed polyp from OC with other colonic endoluminal surface structures such as haustral folds and show that there is a minimum at the correct polyp from CTC.
no code implementations • 22 Jul 2014 • Holger R. Roth, Jianhua Yao, Le Lu, James Stieger, Joseph E. Burns, Ronald M. Summers
In testing, the CNN is employed to assign individual probabilities for a new set of N random views that are averaged at each ROI to compute a final per-candidate classification probability.
no code implementations • 6 Jun 2014 • Holger R. Roth, Le Lu, Ari Seff, Kevin M. Cherry, Joanne Hoffman, Shijun Wang, Jiamin Liu, Evrim Turkbey, Ronald M. Summers
and 84%/90% at 6 FP/vol.