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MLCN/RNO-AI@MICCAI 2020: Lima, Peru
- Seyed Mostafa Kia
, Hassan Mohy-ud-Din
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
MLCN 2020
- Samuel Budd
Surface Agnostic Metrics for Cortical Volume Segmentation and Regression. 3-12 - Gabriele Amorosino
Automatic Tissue Segmentation with Deep Learning in Patients with Congenital or Acquired Distortion of Brain Anatomy. 13-22 - 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. 23-33 - 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. 34-44 - Alexandra Razorenova
Deep Learning for Non-invasive Cortical Potential Imaging. 45-55 - Tommaso Di Noto
An Anatomically-Informed 3D CNN for Brain Aneurysm Classification with Weak Labels. 56-66 - Jianyuan Zhang, Feng Shi
Ischemic Stroke Segmentation from CT Perfusion Scans Using Cluster-Representation Learning. 67-76 - Umar Asif, Subhrajit Roy, Jianbin Tang, Stefan Harrer
SeizureNet: Multi-Spectral Deep Feature Learning for Seizure Type Classification. 77-87 - Yi Hao Chan
Decoding Task States by Spotting Salient Patterns at Time Points and Brain Regions. 88-97 - Kyriaki-Margarita Bintsi, Vasileios Baltatzis, Arinbjörn Kolbeinsson
Patch-Based Brain Age Estimation from MR Images. 98-107 - Étienne Pepin, Jean-Baptiste Carluer, Laurent Chauvin, Matthew Toews
Large-Scale Unbiased Neuroimage Indexing via 3D GPU-SIFT Filtering and Keypoint Masking. 108-118 - Stefano Cerri
A Longitudinal Method for Simultaneous Whole-Brain and Lesion Segmentation in Multiple Sclerosis. 119-128 - Yannick Suter
Towards MRI Progression Features for Glioblastoma Patients: From Automated Volumetry and Classical Radiomics to Deep Feature Learning. 129-138 - Hao Li, Huahong Zhang, Dewei Hu, Hans J. Johnson
Generalizing MRI Subcortical Segmentation to Neurodegeneration. 139-147 - 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. 148-158 - Alena-Kathrin Schnurr
Deep Voxel-Guided Morphometry (VGM): Learning Regional Brain Changes in Serial MRI. 159-168 - 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
A Deep Transfer Learning Framework for 3D Brain Imaging Based on Optimal Mass Transport. 169-176 - Guy Leroy, Daniel Rueckert, Amir Alansary:
Communicative Reinforcement Learning Agents for Landmark Detection in Brain Images. 177-186
RNO-AI 2020
- Sobia Yousaf, Harish RaviPrakash, Syed Muhammad Anwar, Nosheen Sohail, Ulas Bagci:
State-of-the-Art in Brain Tumor Segmentation and Current Challenges. 189-198 - Jay B. Patel, Mishka Gidwani, Ken Chang, Jayashree Kalpathy-Cramer:
Radiomics and Radiogenomics with Deep Learning in Neuro-oncology. 199-211 - Thomas C. Booth
Machine Learning and Glioblastoma: Treatment Response Monitoring Biomarkers in 2021. 212-228 - Navodini Wijethilake
Radiogenomics of Glioblastoma: Identification of Radiomics Associated with Molecular Subtypes. 229-239 - Sonal Gore
Local Binary and Ternary Patterns Based Quantitative Texture Analysis for Assessment of IDH Genotype in Gliomas on Multi-modal MRI. 240-248 - Sadia Anjum, Lal Hussain
Automated Multi-class Brain Tumor Types Detection by Extracting RICA Based Features and Employing Machine Learning Techniques. 249-258 - Asma Shaheen, Stefano Burigat, Ulas Bagci, Hassan Mohy-ud-Din:
Overall Survival Prediction in Gliomas Using Region-Specific Radiomic Features. 259-267 - 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. 268-275 - 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. 276-283 - Sobia Yousaf, Syed Muhammad Anwar, Harish RaviPrakash, Ulas Bagci:
Brain Tumor Survival Prediction Using Radiomics Features. 284-293 - Muhammad Tahir
Brain MRI Classification Using Gradient Boosting. 294-301
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