MLCN/RNO-AI@MICCAI

Publications

2020

Umar Asif, Subhrajit Roy, Jianbin Tang, Stefan Harrer
SeizureNet: Multi-Spectral Deep Feature Learning for Seizure Type Classification. MLCN/RNO-AI@MICCAI (2020)
Batool Rathore, Muhammad Awais, Muhammad Usama Usman, Imran Shafi, Waqas Ahmed
Using Functional Magnetic Resonance Imaging and Personal Characteristics Features for Detection of Neurological Conditions. MLCN/RNO-AI@MICCAI (2020)
Naresh Nandakumar, Niharika Shimona D'Souza, Komal Manzoor, Jay J. Pillai, Sachin K. Gujar, Haris I. Sair, Archana Venkataraman
A Multi-task Deep Learning Framework to Localize the Eloquent Cortex in Brain Tumor Patients Using Dynamic Functional Connectivity. MLCN/RNO-AI@MICCAI (2020)
Thomas C. Booth, Bernice Akpinar, Andrei Roman, Haris Shuaib, Aysha Luis, Alysha Chelliah, Ayisha Al Busaidi, Ayesha Mirchandani, Burcu Alparslan, Nina Mansoor, Keyoumars Ashkan, Sébastien Ourselin, Marc Modat
Machine Learning and Glioblastoma: Treatment Response Monitoring Biomarkers in 2021. MLCN/RNO-AI@MICCAI (2020)
Yi Hao Chan, Sukrit Gupta, L. L. Chamara Kasun, Jagath C. Rajapakse
Decoding Task States by Spotting Salient Patterns at Time Points and Brain Regions. MLCN/RNO-AI@MICCAI (2020)
Matthias Wilms, Jordan J. Bannister, Pauline Mouches, M. Ethan MacDonald, Deepthi Rajashekar, Sönke Langner, Nils D. Forkert
Bidirectional Modeling and Analysis of Brain Aging with Normalizing Flows. MLCN/RNO-AI@MICCAI (2020)
Jianyuan Zhang, Feng Shi, Lei Chen, Zhong Xue, Lichi Zhang, Dahong Qian
Ischemic Stroke Segmentation from CT Perfusion Scans Using Cluster-Representation Learning. MLCN/RNO-AI@MICCAI (2020)
Sadia Anjum, Lal Hussain, Mushtaq Ali, Adeel Ahmed Abbasi
Automated Multi-class Brain Tumor Types Detection by Extracting RICA Based Features and Employing Machine Learning Techniques. MLCN/RNO-AI@MICCAI (2020)
Tommaso Di Noto, Guillaume Marie, Sébastien Tourbier, Yasser Alemán-Gómez, Guillaume Saliou, Meritxell Bach Cuadra, Patric Hagmann, Jonas Richiardi
An Anatomically-Informed 3D CNN for Brain Aneurysm Classification with Weak Labels. MLCN/RNO-AI@MICCAI (2020)
Sobia Yousaf, Harish RaviPrakash, Syed Muhammad Anwar, Nosheen Sohail, Ulas Bagci
State-of-the-Art in Brain Tumor Segmentation and Current Challenges. MLCN/RNO-AI@MICCAI (2020)
Navodini Wijethilake, Mobarakol Islam, Dulani Meedeniya, Charith Chitraranjan, Indika Perera, Hongliang Ren
Radiogenomics of Glioblastoma: Identification of Radiomics Associated with Molecular Subtypes. MLCN/RNO-AI@MICCAI (2020)
Guy Leroy, Daniel Rueckert, Amir Alansary
Communicative Reinforcement Learning Agents for Landmark Detection in Brain Images. MLCN/RNO-AI@MICCAI (2020)
Yannick Suter, Urspeter Knecht, Roland Wiest, Ekkehard Hewer, Philippe Schucht, Mauricio Reyes
Towards MRI Progression Features for Glioblastoma Patients: From Automated Volumetry and Classical Radiomics to Deep Feature Learning. MLCN/RNO-AI@MICCAI (2020)
Gabriele Amorosino, Denis Peruzzo, Pietro Astolfi, Daniela Redaelli, Paolo Avesani, Filippo Arrigoni, Emanuele Olivetti
Automatic Tissue Segmentation with Deep Learning in Patients with Congenital or Acquired Distortion of Brain Anatomy. MLCN/RNO-AI@MICCAI (2020)
Étienne Pepin, Jean-Baptiste Carluer, Laurent Chauvin, Matthew Toews, Rola Harmouche
Large-Scale Unbiased Neuroimage Indexing via 3D GPU-SIFT Filtering and Keypoint Masking. MLCN/RNO-AI@MICCAI (2020)
Ling-Li Zeng, Christopher R. K. Ching, Zvart Abaryan, Sophia I. Thomopoulos, Kai Gao, Alyssa H. Zhu, Anjanibhargavi Ragothaman, Faisal Rashid, Marc Harrison, Lauren E. Salminen, Brandalyn C. Riedel, Neda Jahanshad, Dewen Hu, Paul M. Thompson
A Deep Transfer Learning Framework for 3D Brain Imaging Based on Optimal Mass Transport. MLCN/RNO-AI@MICCAI (2020)
Sonal Gore, Tanay Chougule, Jitender Saini, Madhura Ingalhalikar, Jayant Jagtap
Local Binary and Ternary Patterns Based Quantitative Texture Analysis for Assessment of IDH Genotype in Gliomas on Multi-modal MRI. MLCN/RNO-AI@MICCAI (2020)
Muhammad Tahir
Brain MRI Classification Using Gradient Boosting. MLCN/RNO-AI@MICCAI (2020)
Sergio Tascon-Morales, Stefan Hoffmann, Martin Treiber, Daniel Mensing, Arnau Oliver, Matthias Günther, Johannes Gregori
Multiple Sclerosis Lesion Segmentation Using Longitudinal Normalization and Convolutional Recurrent Neural Networks. MLCN/RNO-AI@MICCAI (2020)
Alena-Kathrin Schnurr, Philipp Eisele, Christina Rossmanith, Stefan Hoffmann, Johannes Gregori, Andreas Dabringhaus, Matthias Kraemer, Raimar Kern, Achim Gass, Frank G. Zöllner
Deep Voxel-Guided Morphometry (VGM): Learning Regional Brain Changes in Serial MRI. MLCN/RNO-AI@MICCAI (2020)
Sobia Yousaf, Syed Muhammad Anwar, Harish RaviPrakash, Ulas Bagci
Brain Tumor Survival Prediction Using Radiomics Features. MLCN/RNO-AI@MICCAI (2020)
Yae Won Park, Ji Eun Park, Sungsoo Ahn, Hwiyoung Kim, Ho Sung Kim, Seung-Koo Lee
Differentiation of Recurrent Glioblastoma from Radiation Necrosis Using Diffusion Radiomics: Machine Learning Model Development and External Validation. MLCN/RNO-AI@MICCAI (2020)
Jay B. Patel, Mishka Gidwani, Ken Chang, Jayashree Kalpathy-Cramer
Radiomics and Radiogenomics with Deep Learning in Neuro-oncology. MLCN/RNO-AI@MICCAI (2020)
Kyriaki-Margarita Bintsi, Vasileios Baltatzis, Arinbjörn Kolbeinsson, Alexander Hammers, Daniel Rueckert
Patch-Based Brain Age Estimation from MR Images. MLCN/RNO-AI@MICCAI (2020)
Stefano Cerri, Andrew Hoopes, Douglas N. Greve, Mark Mühlau, Koen Van Leemput
A Longitudinal Method for Simultaneous Whole-Brain and Lesion Segmentation in Multiple Sclerosis. MLCN/RNO-AI@MICCAI (2020)
Hao Li, Huahong Zhang, Dewei Hu, Hans J. Johnson, Jeffrey D. Long, Jane S. Paulsen, Ipek Oguz
Generalizing MRI Subcortical Segmentation to Neurodegeneration. MLCN/RNO-AI@MICCAI (2020)
Asma Shaheen, Stefano Burigat, Ulas Bagci, Hassan Mohy-ud-Din
Overall Survival Prediction in Gliomas Using Region-Specific Radiomic Features. MLCN/RNO-AI@MICCAI (2020)
Samuel Budd, Prachi A. Patkee, Ana Baburamani, Mary A. Rutherford, Emma C. Robinson, Bernhard Kainz
Surface Agnostic Metrics for Cortical Volume Segmentation and Regression. MLCN/RNO-AI@MICCAI (2020)
Alexandra Razorenova, Nikolay B. Yavich, Mikhail S. Malovichko, Maxim V. Fedorov, Nikolay A. Koshev, Dmitry V. Dylov
Deep Learning for Non-invasive Cortical Potential Imaging. MLCN/RNO-AI@MICCAI (2020)

volume 12449, 2020

Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology - Third International Workshop, MLCN 2020, and Second International Workshop, RNO-AI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4-8, 2020, Proceedings MLCN/RNO-AI@MICCAI 12449 (2020)