default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCID6th Brainles@MICCAI 2020: Lima, Peru - Part II
- Alessandro Crimi
, Spyridon Bakas:
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 6th International Workshop, BrainLes 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Revised Selected Papers, Part II. Lecture Notes in Computer Science 12659, Springer 2021, ISBN 978-3-030-72086-5
Brain Tumor Segmentation
- Tomasz Tarasiewicz, Michal Kawulok, Jakub Nalepa:
Lightweight U-Nets for Brain Tumor Segmentation. 3-14 - Beenitaben Shah, Harish Tayyar Madabushi:
Efficient Brain Tumour Segmentation Using Co-registered Data and Ensembles of Specialised Learners. 15-29 - Mohammadreza Soltaninejad, Tony P. Pridmore, Michael P. Pound:
Efficient MRI Brain Tumor Segmentation Using Multi-resolution Encoder-Decoder Networks. 30-39 - David G. Ellis, Michele R. Aizenberg:
Trialing U-Net Training Modifications for Segmenting Gliomas Using Open Source Deep Learning Framework. 40-49 - Saqib Qamar, Parvez Ahmad, Linlin Shen:
HI-Net: Hyperdense Inception 3D UNet for Brain Tumor Segmentation. 50-57 - Haozhe Jia, Weidong Cai, Heng Huang, Yong Xia:
H2NF-Net for Brain Tumor Segmentation Using Multimodal MR Imaging: 2nd Place Solution to BraTS Challenge 2020 Segmentation Task. 58-68 - Hugh McHugh, Gonzalo D. Maso Talou, Alan Wang:
2D Dense-UNet: A Clinically Valid Approach to Automated Glioma Segmentation. 69-80 - Zi-Jun Su, Tang-Chen Chang, Yen-Ling Tai, Shu-Jung Chang, Chien-Chang Chen:
Attention U-Net with Dimension-Hybridized Fast Data Density Functional Theory for Automatic Brain Tumor Image Segmentation. 81-92 - Changchen Zhao, Zhiming Zhao, Qingrun Zeng, Yuanjing Feng:
MVP U-Net: Multi-View Pointwise U-Net for Brain Tumor Segmentation. 93-103 - Richard Zsamboki, Petra Takacs, Borbála Deák-Karancsi:
Glioma Segmentation with 3D U-Net Backed with Energy-Based Post-Processing. 104-117 - Fabian Isensee, Paul F. Jäger, Peter M. Full, Philipp Vollmuth, Klaus H. Maier-Hein:
nnU-Net for Brain Tumor Segmentation. 118-132 - Sameer Shaikh, Ashish Phophalia:
A Deep Random Forest Approach for Multimodal Brain Tumor Segmentation. 133-147 - Vladimir Groza, Bair Tuchinov, Evgeniya Amelina, Evgeniy N. Pavlovskiy, Nikolay Tolstokulakov, Mikhail Amelin, Sergey Golushko, Andrey Letyagin:
Brain Tumor Segmentation and Associated Uncertainty Evaluation Using Multi-sequences MRI Mixture Data Preprocessing. 148-157 - Shiqiang Ma, Zehua Zhang, Jiaqi Ding, Xuejian Li, Jijun Tang, Fei Guo:
A Deep Supervision CNN Network for Brain Tumor Segmentation. 158-167 - Navchetan Awasthi, Rohit Pardasani, Swati Gupta:
Multi-threshold Attention U-Net (MTAU) Based Model for Multimodal Brain Tumor Segmentation in MRI Scans. 168-178 - Carlos A. Silva, Adriano Pinto, Sérgio Pereira, Ana Lopes:
Multi-stage Deep Layer Aggregation for Brain Tumor Segmentation. 179-188 - Muhammad Junaid Ali, Muhammad Tahir Akram, Hira Saleem, Basit Raza, Ahmad Raza Shahid:
Glioma Segmentation Using Ensemble of 2D/3D U-Nets and Survival Prediction Using Multiple Features Fusion. 189-199 - Lucas Fidon, Sébastien Ourselin, Tom Vercauteren:
Generalized Wasserstein Dice Score, Distributionally Robust Deep Learning, and Ranger for Brain Tumor Segmentation: BraTS 2020 Challenge. 200-214 - Rupal R. Agravat, Mehul S. Raval:
3D Semantic Segmentation of Brain Tumor for Overall Survival Prediction. 215-227 - Jay B. Patel, Ken Chang, Katharina Hoebel, Mishka Gidwani, Nishanth Thumbavanam Arun, Sharut Gupta, Mehak Aggarwal, Praveer Singh, Bruce R. Rosen, Elizabeth R. Gerstner, Jayashree Kalpathy-Cramer:
Segmentation, Survival Prediction, and Uncertainty Estimation of Gliomas from Multimodal 3D MRI Using Selective Kernel Networks. 228-240 - S. Rosas González, Ilyess Zemmoura, Clovis Tauber:
3D Brain Tumor Segmentation and Survival Prediction Using Ensembles of Convolutional Neural Networks. 241-254 - Chinmay Savadikar, Rahul Kulhalli, Bhushan Garware:
Brain Tumour Segmentation Using Probabilistic U-Net. 255-264 - Krzysztof Kotowski, Szymon Adamski, Wojciech Malara, Bartosz Machura, Lukasz Zarudzki, Jakub Nalepa:
Segmenting Brain Tumors from MRI Using Cascaded 3D U-Nets. 265-277 - Jiahua Xu, Wai Po Kevin Teng, Xiong Jun Wang, Andreas Nürnberger:
A Deep Supervised U-Attention Net for Pixel-Wise Brain Tumor Segmentation. 278-289 - Radu Miron, Ramona Albert, Mihaela Breaban:
A Two-Stage Atrous Convolution Neural Network for Brain Tumor Segmentation and Survival Prediction. 290-299 - Keerati Kaewrak, John J. Soraghan, Gaetano Di Caterina, Derek Grose:
TwoPath U-Net for Automatic Brain Tumor Segmentation from Multimodal MRI Data. 300-309 - Vikas Kumar Anand, Sanjeev Grampurohit, Pranav Aurangabadkar, Avinash Kori, Mahendra Khened, Raghavendra S. Bhat, Ganapathy Krishnamurthi:
Brain Tumor Segmentation and Survival Prediction Using Automatic Hard Mining in 3D CNN Architecture. 310-319 - Chase Duncan, Francis Roxas, Neel Jani, Jane Maksimovic, Matthew T. Bramlet, Brad Sutton, Sanmi Koyejo:
Some New Tricks for Deep Glioma Segmentation. 320-330 - Vikas L. Bommineni:
PieceNet: A Redundant UNet Ensemble. 331-341 - Laura Alexandra Daza, Catalina Gómez Caballero, Pablo Arbeláez:
Cerberus: A Multi-headed Network for Brain Tumor Segmentation. 342-351 - Lina Chato, Pushkin Kachroo, Shahram Latifi:
An Automatic Overall Survival Time Prediction System for Glioma Brain Tumor Patients Based on Volumetric and Shape Features. 352-365 - Andrei Iantsen, Vincent Jaouen, Dimitris Visvikis, Mathieu Hatt:
Squeeze-and-Excitation Normalization for Brain Tumor Segmentation. 366-373 - Agus Subhan Akbar, Chastine Fatichah, Nanik Suciati:
Modified MobileNet for Patient Survival Prediction. 374-387 - Mihir Pendse, Vithursan Thangarasa, Vitaliy Chiley, Ryan Holmdahl, Joel Hestness, Dennis DeCoste:
Memory Efficient 3D U-Net with Reversible Mobile Inverted Bottlenecks for Brain Tumor Segmentation. 388-397 - Bhavesh Parmar, Mehul Parikh:
Brain Tumor Segmentation and Survival Prediction Using Patch Based Modified 3D U-Net. 398-409 - Jordan Colman, Lei Zhang, Wenting Duan, Xujiong Ye:
DR-Unet104 for Multimodal MRI Brain Tumor Segmentation. 410-419 - Kun Cheng, Caihao Hu, Pengyu Yin, Qianlan Su, Guancheng Zhou, Xian Wu, Xiaohui Wang, Wei Yang:
Glioma Sub-region Segmentation on Multi-parameter MRI with Label Dropout. 420-430 - Jiarui Tang, Tengfei Li, Hai Shu, Hongtu Zhu:
Variational-Autoencoder Regularized 3D MultiResUNet for the BraTS 2020 Brain Tumor Segmentation. 431-440 - Qiushi Yang, Yixuan Yuan:
Learning Dynamic Convolutions for Multi-modal 3D MRI Brain Tumor Segmentation. 441-451
Computational Precision Medicine: Radiology-Pathology Challenge on Brain Tumor Classification
- Xiyue Wang, Sen Yang, Xiyi Wu:
Automatic Glioma Grading Based on Two-Stage Networks by Integrating Pathology and MRI Images. 455-464 - Baocai Yin, Hu Cheng, Fengyan Wang, Zengfu Wang:
Brain Tumor Classification Based on MRI Images and Noise Reduced Pathology Images. 465-474 - Marvin Lerousseau, Eric Deutsch, Nikos Paragios:
Multimodal Brain Tumor Classification. 475-486 - Linmin Pei, Wei-Wen Hsu, Ling-An Chiang, Jing-Ming Guo, Khan M. Iftekharuddin, Rivka Colen:
A Hybrid Convolutional Neural Network Based-Method for Brain Tumor Classification Using mMRI and WSI. 487-496 - Bingchao Zhao, Jia Huang, Changhong Liang, Zaiyi Liu, Chu Han:
CNN-Based Fully Automatic Glioma Classification with Multi-modal Medical Images. 497-507 - Azam Hamidinekoo, Tomasz Pieciak, Maryam Afzali, Otar Akanyeti, Yinyin Yuan:
Glioma Classification Using Multimodal Radiology and Histology Data. 508-518
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 2025-05-12 21:50 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:
