1 code implementation • 6 May 2024 • Xiwen Chen, Peijie Qiu, Wenhui Zhu, Huayu Li, Hao Wang, Aristeidis Sotiras, Yalin Wang, Abolfazl Razi
Deep neural networks, including transformers and convolutional neural networks, have significantly improved multivariate time series classification (MTSC).
no code implementations • 4 Apr 2024 • Sayantan Kumara, Thomas Earnest, Braden Yang, Deydeep Kothapalli, Tammie L. S. Benzinger, Brian A. Gordon, Philip Payne, Aristeidis Sotiras
ADS individuals with moderate or severe dementia showed higher proportion of regional outliers for each modality as well as more dissimilarity in modality-specific regional outlier patterns compared to ADS individuals with early or mild dementia.
1 code implementation • 29 Mar 2024 • Peijie Qiu, Jin Yang, Sayantan Kumar, Soumyendu Sekhar Ghosh, Aristeidis Sotiras
However, we argue that the current design of the vision transformer-based UNet (ViT-UNet) segmentation models may not effectively handle the heterogeneous appearance (e. g., varying shapes and sizes) of objects of interest in medical image segmentation tasks.
Ranked #2 on Medical Image Segmentation on ACDC
1 code implementation • 15 Mar 2024 • Jin Yang, Peijie Qiu, Yichi Zhang, Daniel S. Marcus, Aristeidis Sotiras
D-Net is able to effectively utilize a multi-scale large receptive field and adaptively harness global contextual information.
no code implementations • 12 Mar 2024 • Jin Yang, Daniel S. Marcus, Aristeidis Sotiras
We evaluated Dynamic U-Net in two abdominal multi-organ segmentation benchmarks.
no code implementations • 2 Dec 2023 • Sayantan Kumar, Philip Payne, Aristeidis Sotiras
Normative models in neuroimaging learn the brain patterns of healthy population distribution and estimate how disease subjects like Alzheimer's Disease (AD) deviate from the norm.
1 code implementation • 31 Oct 2023 • Peijie Qiu, Pan Xiao, Wenhui Zhu, Yalin Wang, Aristeidis Sotiras
In this paper, we proposed a sparsely coded MIL (SC-MIL) that addresses those two aspects at the same time by leveraging sparse dictionary learning.
no code implementations • 1 Jun 2023 • Ahmed W. Moawad, Anastasia Janas, Ujjwal Baid, Divya Ramakrishnan, Leon Jekel, Kiril Krantchev, Harrison Moy, Rachit Saluja, Klara Osenberg, Klara Wilms, Manpreet Kaur, Arman Avesta, Gabriel Cassinelli Pedersen, Nazanin Maleki, Mahdi Salimi, Sarah Merkaj, Marc von Reppert, Niklas Tillmans, Jan Lost, Khaled Bousabarah, Wolfgang Holler, MingDe Lin, Malte Westerhoff, Ryan Maresca, Katherine E. Link, Nourel Hoda Tahon, Daniel Marcus, Aristeidis Sotiras, Pamela Lamontagne, Strajit Chakrabarty, Oleg Teytelboym, Ayda Youssef, Ayaman Nada, Yuri S. Velichko, Nicolo Gennaro, Connectome Students, Group of Annotators, Justin Cramer, Derek R. Johnson, Benjamin Y. M. Kwan, Boyan Petrovic, Satya N. Patro, Lei Wu, Tiffany So, Gerry Thompson, Anthony Kam, Gloria Guzman Perez-Carrillo, Neil Lall, Group of Approvers, Jake Albrecht, Udunna Anazodo, Marius George Lingaru, Bjoern H Menze, Benedikt Wiestler, Maruf Adewole, Syed Muhammad Anwar, Dominic LaBella, Hongwei Bran Li, Juan Eugenio Iglesias, Keyvan Farahani, James Eddy, Timothy Bergquist, Verena Chung, Russel Takeshi Shinohara, Farouk Dako, Walter Wiggins, Zachary Reitman, Chunhao Wang, Xinyang Liu, Zhifan Jiang, Koen van Leemput, Marie Piraud, Ivan Ezhov, Elaine Johanson, Zeke Meier, Ariana Familiar, Anahita Fathi Kazerooni, Florian Kofler, Evan Calabrese, Sanjay Aneja, Veronica Chiang, Ichiro Ikuta, Umber Shafique, Fatima Memon, Gian Marco Conte, Spyridon Bakas, Jeffrey Rudie, Mariam Aboian
Clinical monitoring of metastatic disease to the brain can be a laborious and time-consuming process, especially in cases involving multiple metastases when the assessment is performed manually.
no code implementations • 29 Mar 2023 • Pan Xiao, Peijie Qiu, Sungmin Ha, Abdalla Bani, Shuang Zhou, Aristeidis Sotiras
Several variants of variational autoencoders (VAEs) have been proposed to learn compact data representations by encoding high-dimensional data in a lower dimensional space.
no code implementations • 7 Oct 2022 • Satrajit Chakrabarty, Pamela Lamontagne, Joshua Shimony, Daniel S. Marcus, Aristeidis Sotiras
A 2. 5D hybrid convolutional neural network was proposed to simultaneously localize the tumor and classify its molecular status by leveraging imaging features from MR scans and prior knowledge features from clinical records and tumor location.
2 code implementations • 6 Oct 2022 • Satrajit Chakrabarty, Syed Amaan Abidi, Mina Mousa, Mahati Mokkarala, Isabelle Hren, Divya Yadav, Matthew Kelsey, Pamela Lamontagne, John Wood, Michael Adams, Yuzhuo Su, Sherry Thorpe, Caroline Chung, Aristeidis Sotiras, Daniel S. Marcus
Mean Dice scores were 0. 882 ($\pm$0. 244) and 0. 977 ($\pm$0. 04) for whole tumor segmentation for WUSM and MDA, respectively.
no code implementations • 13 Dec 2021 • Bhakti Baheti, Satrajit Chakrabarty, Hamed Akbari, Michel Bilello, Benedikt Wiestler, Julian Schwarting, Evan Calabrese, Jeffrey Rudie, Syed Abidi, Mina Mousa, Javier Villanueva-Meyer, Brandon K. K. Fields, Florian Kofler, Russell Takeshi Shinohara, Juan Eugenio Iglesias, Tony C. W. Mok, Albert C. S. Chung, Marek Wodzinski, Artur Jurgas, Niccolo Marini, Manfredo Atzori, Henning Muller, Christoph Grobroehmer, Hanna Siebert, Lasse Hansen, Mattias P. Heinrich, Luca Canalini, Jan Klein, Annika Gerken, Stefan Heldmann, Alessa Hering, Horst K. Hahn, Mingyuan Meng, Lei Bi, Dagan Feng, Jinman Kim, Ramy A. Zeineldin, Mohamed E. Karar, Franziska Mathis-Ullrich, Oliver Burgert, Javid Abderezaei, Aymeric Pionteck, Agamdeep Chopra, Mehmet Kurt, Kewei Yan, Yonghong Yan, Zhe Tang, Jianqiang Ma, Sahar Almahfouz Nasser, Nikhil Cherian Kurian, Mohit Meena, Saqib Shamsi, Amit Sethi, Nicholas J. Tustison, Brian B. Avants, Philip Cook, James C. Gee, Lin Tian, Hastings Greer, Marc Niethammer, Andrew Hoopes, Malte Hoffmann, Adrian V. Dalca, Stergios Christodoulidis, Theo Estiene, Maria Vakalopoulou, Nikos Paragios, Daniel S. Marcus, Christos Davatzikos, Aristeidis Sotiras, Bjoern Menze, Spyridon Bakas, Diana Waldmannstetter
Registration of longitudinal brain MRI scans containing pathologies is challenging due to dramatic changes in tissue appearance.
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 • 10 Oct 2021 • Sayantan Kumar, Philip Payne, Aristeidis Sotiras
However, existing deep learning based normative models on multimodal MRI data use unimodal autoencoders with a single encoder and decoder that may fail to capture the relationship between brain measurements extracted from different MRI modalities.
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).
1 code implementation • 1 Jul 2020 • Junhao Wen, Erdem Varol, Ganesh Chand, Aristeidis Sotiras, Christos Davatzikos
There is a growing amount of clinical, anatomical and functional evidence for the heterogeneous presentation of neuropsychiatric and neurodegenerative diseases such as schizophrenia and Alzheimers Disease (AD).
4 code implementations • 9 Feb 2020 • Razvan V. Marinescu, Neil P. Oxtoby, Alexandra L. Young, Esther E. Bron, Arthur W. Toga, Michael W. Weiner, Frederik Barkhof, Nick C. Fox, Arman Eshaghi, Tina Toni, Marcin Salaterski, Veronika Lunina, Manon Ansart, Stanley Durrleman, Pascal Lu, Samuel Iddi, Dan Li, Wesley K. Thompson, Michael C. Donohue, Aviv Nahon, Yarden Levy, Dan Halbersberg, Mariya Cohen, Huiling Liao, Tengfei Li, Kaixian Yu, Hongtu Zhu, Jose G. Tamez-Pena, Aya Ismail, Timothy Wood, Hector Corrada Bravo, Minh Nguyen, Nanbo Sun, Jiashi Feng, B. T. Thomas Yeo, Gang Chen, Ke Qi, Shiyang Chen, Deqiang Qiu, Ionut Buciuman, Alex Kelner, Raluca Pop, Denisa Rimocea, Mostafa M. Ghazi, Mads Nielsen, Sebastien Ourselin, Lauge Sorensen, Vikram Venkatraghavan, Keli Liu, Christina Rabe, Paul Manser, Steven M. Hill, James Howlett, Zhiyue Huang, Steven Kiddle, Sach Mukherjee, Anais Rouanet, Bernd Taschler, Brian D. M. Tom, Simon R. White, Noel Faux, Suman Sedai, Javier de Velasco Oriol, Edgar E. V. Clemente, Karol Estrada, Leon Aksman, Andre Altmann, Cynthia M. Stonnington, Yalin Wang, Jianfeng Wu, Vivek Devadas, Clementine Fourrier, Lars Lau Raket, Aristeidis Sotiras, Guray Erus, Jimit Doshi, Christos Davatzikos, Jacob Vogel, Andrew Doyle, Angela Tam, Alex Diaz-Papkovich, Emmanuel Jammeh, Igor Koval, Paul Moore, Terry J. Lyons, John Gallacher, Jussi Tohka, Robert Ciszek, Bruno Jedynak, Kruti Pandya, Murat Bilgel, William Engels, Joseph Cole, Polina Golland, Stefan Klein, Daniel C. Alexander
TADPOLE's unique results suggest that current prediction algorithms provide sufficient accuracy to exploit biomarkers related to clinical diagnosis and ventricle volume, for cohort refinement in clinical trials for Alzheimer's disease.