Search Results for author: Aristeidis Sotiras

Found 12 papers, 5 papers with code

Improving Normative Modeling for Multi-modal Neuroimaging Data using mixture-of-product-of-experts variational autoencoders

no code implementations2 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.

SC-MIL: Sparsely Coded Multiple Instance Learning for Whole Slide Image Classification

1 code implementation31 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.

Dictionary Learning Image Classification +1

The Brain Tumor Segmentation (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI

no code implementations1 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.

Brain Tumor Segmentation Decision Making +2

SC-VAE: Sparse Coding-based Variational Autoencoder with Learned ISTA

no code implementations29 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.

Image Generation Image Reconstruction +5

MRI-based classification of IDH mutation and 1p/19q codeletion status of gliomas using a 2.5D hybrid multi-task convolutional neural network

no code implementations7 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.

Brain Tumor Segmentation Management +1

Normative Modeling using Multimodal Variational Autoencoders to Identify Abnormal Brain Structural Patterns in Alzheimer Disease

no code implementations10 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.

GPR

MAGIC: Multi-scale Heterogeneity Analysis and Clustering for Brain Diseases

1 code implementation1 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).

Clustering Hippocampus

The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up

4 code implementations9 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.

Alzheimer's Disease Detection Disease Prediction

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