no code implementations • 6 Mar 2023 • Hao Zheng, Hongming Li, Yong Fan
Different from existing deep learning-based cortical surface reconstruction methods that either reconstruct the cortical surfaces separately or neglect the interdependence between the inner and outer surfaces, SurfNN reconstructs both the inner and outer cortical surfaces jointly by training a single network to predict a midthickness surface that lies at the center of the inner and outer cortical surfaces.
no code implementations • 27 Jan 2023 • BoJian Hou, Hongming Li, Zhicheng Jiao, Zhen Zhou, Hao Zheng, Yong Fan
We learn weights of the expert distributions for individual instances according to their features discriminatively such that each instance's survival information can be characterized by a weighted combination of the learned constant expert distributions.
no code implementations • 6 Jul 2022 • Yuemeng Li, Hee Kwon Song, Miguel Romanello Joaquim, Stephen Pickup, Rong Zhou, Yong Fan
Purpose: To accelerate radially sampled diffusion weighted spin-echo (Rad-DW-SE) acquisition method for generating high quality of apparent diffusion coefficient (ADC) maps.
1 code implementation • 20 Oct 2021 • Junhao Wen, Cynthia H. Y. Fu, Duygu Tosun, Yogasudha Veturi, Zhijian Yang, Ahmed Abdulkadir, Elizabeth Mamourian, Dhivya Srinivasan, Jingxuan Bao, Guray Erus, Haochang Shou, Mohamad Habes, Jimit Doshi, Erdem Varol, Scott R Mackin, Aristeidis Sotiras, Yong Fan, Andrew J. Saykin, Yvette I. Sheline, Li Shen, Marylyn D. Ritchie, David A. Wolk, Marilyn Albert, Susan M. Resnick, Christos Davatzikos
We sought to delineate, cross-sectionally and longitudinally, disease-related heterogeneity in LLD linked to neuroanatomy, cognitive functioning, clinical symptomatology, and genetic profiles.
no code implementations • 8 Sep 2021 • Gyujoon Hwang, Ahmed Abdulkadir, Guray Erus, Mohamad Habes, Raymond Pomponio, Haochang Shou, Jimit Doshi, Elizabeth Mamourian, Tanweer Rashid, Murat Bilgel, Yong Fan, Aristeidis Sotiras, Dhivya Srinivasan, John C. Morris, Daniel Marcus, Marilyn S. Albert, Nick R. Bryan, Susan M. Resnick, Ilya M. Nasrallah, Christos Davatzikos, David A. Wolk
First, a subset of AD patients and CN adults were selected based purely on clinical diagnoses to train SPARE-BA1 (regression of age using CN individuals) and SPARE-AD1 (classification of CN versus AD).
3 code implementations • 26 Feb 2021 • Sarthak Pati, Siddhesh P. Thakur, İbrahim Ethem Hamamcı, Ujjwal Baid, Bhakti Baheti, Megh Bhalerao, Orhun Güley, Sofia Mouchtaris, David Lang, Spyridon Thermos, Karol Gotkowski, Camila González, Caleb Grenko, Alexander Getka, Brandon Edwards, Micah Sheller, Junwen Wu, Deepthi Karkada, Ravi Panchumarthy, Vinayak Ahluwalia, Chunrui Zou, Vishnu Bashyam, Yuemeng Li, Babak Haghighi, Rhea Chitalia, Shahira Abousamra, Tahsin M. Kurc, Aimilia Gastounioti, Sezgin Er, Mark Bergman, Joel H. Saltz, Yong Fan, Prashant Shah, Anirban Mukhopadhyay, Sotirios A. Tsaftaris, Bjoern Menze, Christos Davatzikos, Despina Kontos, Alexandros Karargyris, Renato Umeton, Peter Mattson, Spyridon Bakas
Deep Learning (DL) has the potential to optimize machine learning in both the scientific and clinical communities.
no code implementations • 11 Dec 2020 • Hongming Li, Yong Fan
A novel unsupervised deep learning method is developed to identify individual-specific large scale brain functional networks (FNs) from resting-state fMRI (rsfMRI) in an end-to-end learning fashion.
1 code implementation • 3 Nov 2020 • Jiacheng Wang, Yong Fan, Duo Jiang, Shiqing Li
Neural network has been recognized with its accomplishments on tackling various natural language understanding (NLU) tasks.
no code implementations • 11 Oct 2020 • Vishnu M. Bashyam, Jimit Doshi, Guray Erus, Dhivya Srinivasan, Ahmed Abdulkadir, Mohamad Habes, Yong Fan, Colin L. Masters, Paul Maruff, Chuanjun Zhuo, Henry Völzke, Sterling C. Johnson, Jurgen Fripp, Nikolaos Koutsouleris, Theodore D. Satterthwaite, Daniel H. Wolf, Raquel E. Gur, Ruben C. Gur, John C. Morris, Marilyn S. Albert, Hans J. Grabe, Susan M. Resnick, R. Nick Bryan, David A. Wolk, Haochang Shou, Ilya M. Nasrallah, Christos Davatzikos
Conventional and deep learning-based methods have shown great potential in the medical imaging domain, as means for deriving diagnostic, prognostic, and predictive biomarkers, and by contributing to precision medicine.
no code implementations • 4 Oct 2020 • Hongming Li, Yong Fan
We present a diffeomorphic image registration algorithm to learn spatial transformations between pairs of images to be registered using fully convolutional networks (FCNs) under a self-supervised learning setting.
no code implementations • 2 Jun 2020 • Tianming Du, Honggang Zhang, Yuemeng Li, Hee Kwon Song, Yong Fan
Deep learning in k-space has demonstrated great potential for image reconstruction from undersampled k-space data in fast magnetic resonance imaging (MRI).
1 code implementation • 13 Feb 2020 • Yuemeng Li, Hongming Li, Yong Fan
However, existing 2D deep learning methods are not equipped to effectively capture 3D spatial contextual information that is needed to achieve accurate brain structure segmentation.
no code implementations • 23 Oct 2019 • Zhen Liu, Borui Xiao, Yuemeng Li, Yong Fan
Skull stripping is usually the first step for most brain analysisprocess in magnetic resonance images.
no code implementations • 7 May 2019 • Yuemeng Li, Hangfan Liu, Hongming Li, Yong Fan
In this way, the network is guaranteed to be aware of the class-dependent feature maps to facilitate the segmentation.
no code implementations • 15 Apr 2019 • Hongming Li, Mohamad Habes, David A. Wolk, Yong Fan
Introduction: It is challenging at baseline to predict when and which individuals who meet criteria for mild cognitive impairment (MCI) will ultimately progress to Alzheimer's disease (AD) dementia.
2 code implementations • 6 Apr 2019 • Yuemeng Li, Yong Fan
Pulmonary nodule detection plays an important role in lung cancer screening with low-dose computed tomography (CT) scans.
Automated Pulmonary Nodule Detection And Classification Computed Tomography (CT) +2
no code implementations • 5 Jan 2019 • Shi Yin, Zhengqiang Zhang, Hongming Li, Qinmu Peng, Xinge You, Susan L. Furth, Gregory E. Tasian, Yong Fan
It remains challenging to automatically segment kidneys in clinical ultrasound images due to the kidneys' varied shapes and image intensity distributions, although semi-automatic methods have achieved promising performance.
no code implementations • 5 Jan 2019 • Hongming Li, Pamela Boimel, James Janopaul-Naylor, Haoyu Zhong, Ying Xiao, Edgar Ben-Josef, Yong Fan
To improve existing survival analysis techniques whose performance is hinged on imaging features, we propose a deep learning method to build survival regression models by optimizing imaging features with deep convolutional neural networks (CNNs) in a proportional hazards model.
no code implementations • 5 Jan 2019 • Hongming Li, Yong Fan
Multi-modal biological, imaging, and neuropsychological markers have demonstrated promising performance for distinguishing Alzheimer's disease (AD) patients from cognitively normal elders.
no code implementations • 12 Nov 2018 • Shi Yin, Qinmu Peng, Hongming Li, Zhengqiang Zhang, Xinge You, Susan L. Furth, Gregory E. Tasian, Yong Fan
It remains challenging to automatically segment kidneys in clinical ultrasound (US) images due to the kidneys' varied shapes and image intensity distributions, although semi-automatic methods have achieved promising performance.
1 code implementation • 5 Nov 2018 • Spyridon Bakas, Mauricio Reyes, Andras Jakab, Stefan Bauer, Markus Rempfler, Alessandro Crimi, Russell Takeshi Shinohara, Christoph Berger, Sung Min Ha, Martin Rozycki, Marcel Prastawa, Esther Alberts, Jana Lipkova, John Freymann, Justin Kirby, Michel Bilello, Hassan Fathallah-Shaykh, Roland Wiest, Jan Kirschke, Benedikt Wiestler, Rivka Colen, Aikaterini Kotrotsou, Pamela Lamontagne, Daniel Marcus, Mikhail Milchenko, Arash Nazeri, Marc-Andre Weber, Abhishek Mahajan, Ujjwal Baid, Elizabeth Gerstner, Dongjin Kwon, Gagan Acharya, Manu Agarwal, Mahbubul Alam, Alberto Albiol, Antonio Albiol, Francisco J. Albiol, Varghese Alex, Nigel Allinson, Pedro H. A. Amorim, Abhijit Amrutkar, Ganesh Anand, Simon Andermatt, Tal Arbel, Pablo Arbelaez, Aaron Avery, Muneeza Azmat, Pranjal B., W Bai, Subhashis Banerjee, Bill Barth, Thomas Batchelder, Kayhan Batmanghelich, Enzo Battistella, Andrew Beers, Mikhail Belyaev, Martin Bendszus, Eze Benson, Jose Bernal, Halandur Nagaraja Bharath, George Biros, Sotirios Bisdas, James Brown, Mariano Cabezas, Shilei Cao, Jorge M. Cardoso, Eric N Carver, Adrià Casamitjana, Laura Silvana Castillo, Marcel Catà, Philippe Cattin, Albert Cerigues, Vinicius S. Chagas, Siddhartha Chandra, Yi-Ju Chang, Shiyu Chang, Ken Chang, Joseph Chazalon, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Chen, Kun Cheng, Ahana Roy Choudhury, Roger Chylla, Albert Clérigues, Steven Colleman, Ramiro German Rodriguez Colmeiro, Marc Combalia, Anthony Costa, Xiaomeng Cui, Zhenzhen Dai, Lutao Dai, Laura Alexandra Daza, Eric Deutsch, Changxing Ding, Chao Dong, Shidu Dong, Wojciech Dudzik, Zach Eaton-Rosen, Gary Egan, Guilherme Escudero, Théo Estienne, Richard Everson, Jonathan Fabrizio, Yong Fan, Longwei Fang, Xue Feng, Enzo Ferrante, Lucas Fidon, Martin Fischer, Andrew P. French, Naomi Fridman, Huan Fu, David Fuentes, Yaozong Gao, Evan Gates, David Gering, Amir Gholami, Willi Gierke, Ben Glocker, Mingming Gong, Sandra González-Villá, T. Grosges, Yuanfang Guan, Sheng Guo, Sudeep Gupta, Woo-Sup Han, Il Song Han, Konstantin Harmuth, Huiguang He, Aura Hernández-Sabaté, Evelyn Herrmann, Naveen Himthani, Winston Hsu, Cheyu Hsu, Xiaojun Hu, Xiaobin Hu, Yan Hu, Yifan Hu, Rui Hua, Teng-Yi Huang, Weilin Huang, Sabine Van Huffel, Quan Huo, Vivek HV, Khan M. Iftekharuddin, Fabian Isensee, Mobarakol Islam, Aaron S. Jackson, Sachin R. Jambawalikar, Andrew Jesson, Weijian Jian, Peter Jin, V Jeya Maria Jose, Alain Jungo, B Kainz, Konstantinos Kamnitsas, Po-Yu Kao, Ayush Karnawat, Thomas Kellermeier, Adel Kermi, Kurt Keutzer, Mohamed Tarek Khadir, Mahendra Khened, Philipp Kickingereder, Geena Kim, Nik King, Haley Knapp, Urspeter Knecht, Lisa Kohli, Deren Kong, Xiangmao Kong, Simon Koppers, Avinash Kori, Ganapathy Krishnamurthi, Egor Krivov, Piyush Kumar, Kaisar Kushibar, Dmitrii Lachinov, Tryphon Lambrou, Joon Lee, Chengen Lee, Yuehchou Lee, M Lee, Szidonia Lefkovits, Laszlo Lefkovits, James Levitt, Tengfei Li, Hongwei Li, Hongyang Li, Xiaochuan Li, Yuexiang Li, Heng Li, Zhenye Li, Xiaoyu Li, Zeju Li, Xiaogang Li, Wenqi Li, Zheng-Shen Lin, Fengming Lin, Pietro Lio, Chang Liu, Boqiang Liu, Xiang Liu, Mingyuan Liu, Ju Liu, Luyan Liu, Xavier Llado, Marc Moreno Lopez, Pablo Ribalta Lorenzo, Zhentai Lu, Lin Luo, Zhigang Luo, Jun Ma, Kai Ma, Thomas Mackie, Anant Madabushi, Issam Mahmoudi, Klaus H. Maier-Hein, Pradipta Maji, CP Mammen, Andreas Mang, B. S. Manjunath, Michal Marcinkiewicz, S McDonagh, Stephen McKenna, Richard McKinley, Miriam Mehl, Sachin Mehta, Raghav Mehta, Raphael Meier, Christoph Meinel, Dorit Merhof, Craig Meyer, Robert Miller, Sushmita Mitra, Aliasgar Moiyadi, David Molina-Garcia, Miguel A. B. Monteiro, Grzegorz Mrukwa, Andriy Myronenko, Jakub Nalepa, Thuyen Ngo, Dong Nie, Holly Ning, Chen Niu, Nicholas K Nuechterlein, Eric Oermann, Arlindo Oliveira, Diego D. C. Oliveira, Arnau Oliver, Alexander F. I. Osman, Yu-Nian Ou, Sebastien Ourselin, Nikos Paragios, Moo Sung Park, Brad Paschke, J. Gregory Pauloski, Kamlesh Pawar, Nick Pawlowski, Linmin Pei, Suting Peng, Silvio M. Pereira, Julian Perez-Beteta, Victor M. Perez-Garcia, Simon Pezold, Bao Pham, Ashish Phophalia, Gemma Piella, G. N. Pillai, Marie Piraud, Maxim Pisov, Anmol Popli, Michael P. Pound, Reza Pourreza, Prateek Prasanna, Vesna Prkovska, Tony P. Pridmore, Santi Puch, Élodie Puybareau, Buyue Qian, Xu Qiao, Martin Rajchl, Swapnil Rane, Michael Rebsamen, Hongliang Ren, Xuhua Ren, Karthik Revanuru, Mina Rezaei, Oliver Rippel, Luis Carlos Rivera, Charlotte Robert, Bruce Rosen, Daniel Rueckert, Mohammed Safwan, Mostafa Salem, Joaquim Salvi, Irina Sanchez, Irina Sánchez, Heitor M. Santos, Emmett Sartor, Dawid Schellingerhout, Klaudius Scheufele, Matthew R. Scott, Artur A. Scussel, Sara Sedlar, Juan Pablo Serrano-Rubio, N. Jon Shah, Nameetha Shah, Mazhar Shaikh, B. Uma Shankar, Zeina Shboul, Haipeng Shen, Dinggang Shen, Linlin Shen, Haocheng Shen, Varun Shenoy, Feng Shi, Hyung Eun Shin, Hai Shu, Diana Sima, M Sinclair, Orjan Smedby, James M. Snyder, Mohammadreza Soltaninejad, Guidong Song, Mehul Soni, Jean Stawiaski, Shashank Subramanian, Li Sun, Roger Sun, Jiawei Sun, Kay Sun, Yu Sun, Guoxia Sun, Shuang Sun, Yannick R Suter, Laszlo Szilagyi, Sanjay Talbar, DaCheng Tao, Zhongzhao Teng, Siddhesh Thakur, Meenakshi H Thakur, Sameer Tharakan, Pallavi Tiwari, Guillaume Tochon, Tuan Tran, Yuhsiang M. Tsai, Kuan-Lun Tseng, Tran Anh Tuan, Vadim Turlapov, Nicholas Tustison, Maria Vakalopoulou, Sergi Valverde, Rami Vanguri, Evgeny Vasiliev, Jonathan Ventura, Luis Vera, Tom Vercauteren, C. A. Verrastro, Lasitha Vidyaratne, Veronica Vilaplana, Ajeet Vivekanandan, Qian Wang, Chiatse J. Wang, Wei-Chung Wang, Duo Wang, Ruixuan Wang, Yuanyuan Wang, Chunliang Wang, Guotai Wang, Ning Wen, Xin Wen, Leon Weninger, Wolfgang Wick, Shaocheng Wu, Qiang Wu, Yihong Wu, Yong Xia, Yanwu Xu, Xiaowen Xu, Peiyuan Xu, Tsai-Ling Yang, Xiaoping Yang, Hao-Yu Yang, Junlin Yang, Haojin Yang, Guang Yang, Hongdou Yao, Xujiong Ye, Changchang Yin, Brett Young-Moxon, Jinhua Yu, Xiangyu Yue, Songtao Zhang, Angela Zhang, Kun Zhang, Xue-jie Zhang, Lichi Zhang, Xiaoyue Zhang, Yazhuo Zhang, Lei Zhang, Jian-Guo Zhang, Xiang Zhang, Tianhao Zhang, Sicheng Zhao, Yu Zhao, Xiaomei Zhao, Liang Zhao, Yefeng Zheng, Liming Zhong, Chenhong Zhou, Xiaobing Zhou, Fan Zhou, Hongtu Zhu, Jin Zhu, Ying Zhuge, Weiwei Zong, Jayashree Kalpathy-Cramer, Keyvan Farahani, Christos Davatzikos, Koen van Leemput, Bjoern Menze
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
no code implementations • 14 Sep 2018 • Hongming Li, Yong Fan
Dynamic functional connectivity analysis provides valuable information for understanding brain functional activity underlying different cognitive processes.
no code implementations • 14 Sep 2018 • Hongming Li, Xiaofeng Zhu, Yong Fan
We present a deep semi-nonnegative matrix factorization method for identifying subject-specific functional networks (FNs) at multiple spatial scales with a hierarchical organization from resting state fMRI data.
no code implementations • 14 Sep 2018 • Hongming Li, Yong Fan
Decoding brain functional states underlying different cognitive processes using multivariate pattern recognition techniques has attracted increasing interests in brain imaging studies.
no code implementations • 11 Jan 2018 • Hongming Li, Theodore D. Satterthwaite, Yong Fan
Whole brain voxel-wise FC measures could provide fine-grained FC information of the brain and may improve the prediction performance.
no code implementations • 11 Jan 2018 • Hongming Li, Yong Fan
A novel non-rigid image registration algorithm is built upon fully convolutional networks (FCNs) to optimize and learn spatial transformations between pairs of images to be registered in a self-supervised learning framework.
no code implementations • 31 Dec 2017 • Qiang Zheng, Yong Fan
The semi-supervised label propagation method takes into consideration local and global image appearance of images to be segmented and segments the images by propagating reliable segmentation results obtained by the supervised random forests method.
no code implementations • 31 Dec 2017 • Qiang Zheng, Gregory Tasian, Yong Fan
In this study, we propose a transfer learning-based method to extract imaging features from US kidney images in order to improve the CAKUT diagnosis in children.
no code implementations • 5 Sep 2017 • Hongming Li, Mohamad Habes, Yong Fan
Increasing effort in brain image analysis has been dedicated to early diagnosis of Alzheimer's disease (AD) based on neuroimaging data.
1 code implementation • 4 Sep 2017 • Hongming Li, Yong Fan
We propose a novel non-rigid image registration algorithm that is built upon fully convolutional networks (FCNs) to optimize and learn spatial transformations between pairs of images to be registered.
no code implementations • 11 Jun 2017 • Qiang Zheng, Steven Warner, Gregory Tasian, Yong Fan
The proposed method has been evaluated and compared with state of the art image segmentation methods based on clinical kidney US images of 85 subjects.
no code implementations • 15 Feb 2017 • Xiaomei Zhao, Yihong Wu, Guidong Song, Zhenye Li, Yazhuo Zhang, Yong Fan
We train a deep learning based segmentation model using 2D image patches and image slices in following steps: 1) training FCNNs using image patches; 2) training CRFs as Recurrent Neural Networks (CRF-RNN) using image slices with parameters of FCNNs fixed; and 3) fine-tuning the FCNNs and the CRF-RNN using image slices.
no code implementations • 2 Apr 2015 • Hanyang Peng, Yong Fan
In this paper, we propose a novel sparse learning based feature selection method that directly optimizes a large margin linear classification model sparsity with l_(2, p)-norm (0 < p < 1)subject to data-fitting constraints, rather than using the sparsity as a regularization term.