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Publication search results
found 30 matches
- 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: 13-22 - 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: 249-258 - Umar Asif, Subhrajit Roy, Jianbin Tang, Stefan Harrer
:
SeizureNet: Multi-Spectral Deep Feature Learning for Seizure Type Classification. MLCN/RNO-AI@MICCAI 2020: 77-87 - Kyriaki-Margarita Bintsi, Vasileios Baltatzis, Arinbjörn Kolbeinsson
, Alexander Hammers
, Daniel Rueckert:
Patch-Based Brain Age Estimation from MR Images. MLCN/RNO-AI@MICCAI 2020: 98-107 - 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: 212-228 - 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: 3-12 - 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: 119-128 - 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: 88-97 - 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: 240-248 - Guy Leroy, Daniel Rueckert, Amir Alansary:
Communicative Reinforcement Learning Agents for Landmark Detection in Brain Images. MLCN/RNO-AI@MICCAI 2020: 177-186 - 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: 139-147 - 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: 34-44 - 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: 56-66 - 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: 276-283 - Jay B. Patel, Mishka Gidwani, Ken Chang, Jayashree Kalpathy-Cramer:
Radiomics and Radiogenomics with Deep Learning in Neuro-oncology. MLCN/RNO-AI@MICCAI 2020: 199-211 - 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: 108-118 - 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: 268-275 - 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: 45-55 - 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: 159-168 - 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: 259-267 - 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: 129-138 - Muhammad Tahir
:
Brain MRI Classification Using Gradient Boosting. MLCN/RNO-AI@MICCAI 2020: 294-301 - 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: 148-158 - 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: 229-239 - 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: 23-33 - Sobia Yousaf, Syed Muhammad Anwar, Harish RaviPrakash, Ulas Bagci:
Brain Tumor Survival Prediction Using Radiomics Features. MLCN/RNO-AI@MICCAI 2020: 284-293 - 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: 189-198 - 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: 169-176 - 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: 67-76 - Seyed Mostafa Kia
, Hassan Mohy-ud-Din
, Ahmed Abdulkadir
, Cher Bass
, Mohamad Habes
, Jane Maryam Rondina
, Chantal M. W. Tax
, Hongzhi Wang
, Thomas Wolfers
, Saima Rathore, Madhura Ingalhalikar:
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. Lecture Notes in Computer Science 12449, Springer 2020, ISBN 978-3-030-66842-6 [contents]
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