Stop the war!Остановите войну!
for scientists:
default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDMLCN/RNO-AI@MICCAI 2020: Lima, Peru
- 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
MLCN 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. 3-12 - 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. 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, Nikolay B. Yavich, Mikhail S. Malovichko, Maxim V. Fedorov, Nikolay A. Koshev, Dmitry V. Dylov:
Deep Learning for Non-invasive Cortical Potential Imaging. 45-55 - 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. 56-66 - Jianyuan Zhang, Feng Shi, Lei Chen, Zhong Xue, Lichi Zhang, Dahong Qian:
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, Sukrit Gupta, L. L. Chamara Kasun, Jagath C. Rajapakse:
Decoding Task States by Spotting Salient Patterns at Time Points and Brain Regions. 88-97 - Kyriaki-Margarita Bintsi, Vasileios Baltatzis, Arinbjörn Kolbeinsson, Alexander Hammers, Daniel Rueckert:
Patch-Based Brain Age Estimation from MR Images. 98-107 - Étienne Pepin, Jean-Baptiste Carluer, Laurent Chauvin, Matthew Toews, Rola Harmouche:
Large-Scale Unbiased Neuroimage Indexing via 3D GPU-SIFT Filtering and Keypoint Masking. 108-118 - 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. 119-128 - 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. 129-138 - Hao Li, Huahong Zhang, Dewei Hu, Hans J. Johnson, Jeffrey D. Long, Jane S. Paulsen, Ipek Oguz:
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, 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. 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, Brandalyn C. Riedel, Neda Jahanshad, Dewen Hu, Paul M. Thompson:
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, 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. 212-228 - Navodini Wijethilake, Mobarakol Islam, Dulani Meedeniya, Charith Chitraranjan, Indika Perera, Hongliang Ren:
Radiogenomics of Glioblastoma: Identification of Radiomics Associated with Molecular Subtypes. 229-239 - 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. 240-248 - 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. 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
a service of 
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
last updated on 2024-04-22 20:00 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
