default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCID4th Brainles@MICCAI 2018: Granada, Spain
- Alessandro Crimi, Spyridon Bakas, Hugo J. Kuijf, Farahani Keyvan, Mauricio Reyes, Theo van Walsum:
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Revised Selected Papers, Part II. Lecture Notes in Computer Science 11384, Springer 2019, ISBN 978-3-030-11725-2
Brain Tumor Image Segmentation
- Leon Weninger, Oliver Rippel, Simon Koppers, Dorit Merhof:
Segmentation of Brain Tumors and Patient Survival Prediction: Methods for the BraTS 2018 Challenge. 3-12 - Michal Marcinkiewicz, Jakub Nalepa, Pablo Ribalta Lorenzo, Wojciech Dudzik, Grzegorz Mrukwa
:
Segmenting Brain Tumors from MRI Using Cascaded Multi-modal U-Nets. 13-24 - Jun Ma, Xiaoping Yang:
Automatic Brain Tumor Segmentation by Exploring the Multi-modality Complementary Information and Cascaded 3D Lightweight CNNs. 25-36 - Adel Kermi, Issam Mahmoudi, Mohamed Tarek Khadir:
Deep Convolutional Neural Networks Using U-Net for Automatic Brain Tumor Segmentation in Multimodal MRI Volumes. 37-48 - Rui Hua, Quan Huo, Yaozong Gao, Yu Sun, Feng Shi:
Multimodal Brain Tumor Segmentation Using Cascaded V-Nets. 49-60 - Guotai Wang, Wenqi Li, Sébastien Ourselin, Tom Vercauteren:
Automatic Brain Tumor Segmentation Using Convolutional Neural Networks with Test-Time Augmentation. 61-72 - Alberto Albiol, Antonio Albiol, Francisco Albiol:
Extending 2D Deep Learning Architectures to 3D Image Segmentation Problems. 73-82 - Li Sun, Songtao Zhang, Lin Luo:
Tumor Segmentation and Survival Prediction in Glioma with Deep Learning. 83-93 - Subhashis Banerjee, Sushmita Mitra, B. Uma Shankar:
Multi-planar Spatial-ConvNet for Segmentation and Survival Prediction in Brain Cancer. 94-104 - Jean Stawiaski:
A Pretrained DenseNet Encoder for Brain Tumor Segmentation. 105-115 - Xiaobin Hu, Hongwei Li, Yu Zhao, Chao Dong, Bjoern H. Menze, Marie Piraud:
Hierarchical Multi-class Segmentation of Glioma Images Using Networks with Multi-level Activation Function. 116-127 - Po-Yu Kao, Thuyen Ngo, Angela Zhang, Jefferson W. Chen, B. S. Manjunath:
Brain Tumor Segmentation and Tractographic Feature Extraction from Structural MR Images for Overall Survival Prediction. 128-141 - Mobarakol Islam, V. Jeya Maria Jose, Hongliang Ren:
Glioma Prognosis: Segmentation of the Tumor and Survival Prediction Using Shape, Geometric and Clinical Information. 142-153 - Ahana Roy Choudhury, Rami Vanguri, Sachin R. Jambawalikar, Piyush Kumar:
Segmentation of Brain Tumors Using DeepLabv3+. 154-167 - Yan Hu, Xiang Liu, Xin Wen, Chen Niu, Yong Xia:
Brain Tumor Segmentation on Multimodal MR Imaging Using Multi-level Upsampling in Decoder. 168-177 - Woo-Sup Han, Il Song Han:
Neuromorphic Neural Network for Multimodal Brain Image Segmentation and Overall Survival Analysis. 178-188 - Dmitry A. Lachinov, Evgeny Vasiliev, Vadim Turlapov:
Glioma Segmentation with Cascaded UNet. 189-198 - Élodie Puybareau, Guillaume Tochon, Joseph Chazalon, Jonathan Fabrizio:
Segmentation of Gliomas and Prediction of Patient Overall Survival: A Simple and Fast Procedure. 199-209 - Juan Pablo Serrano Rubio, Richard M. Everson:
Brain Tumour Segmentation Method Based on Supervoxels and Sparse Dictionaries. 210-221 - Yanwu Xu, Mingming Gong, Huan Fu, Dacheng Tao, Kun Zhang, Kayhan Batmanghelich:
Multi-scale Masked 3-D U-Net for Brain Tumor Segmentation. 222-233 - Fabian Isensee, Philipp Kickingereder, Wolfgang Wick, Martin Bendszus, Klaus H. Maier-Hein:
No New-Net. 234-244 - Nicholas Nuechterlein, Sachin Mehta:
3D-ESPNet with Pyramidal Refinement for Volumetric Brain Tumor Image Segmentation. 245-253 - Raghav Mehta, Tal Arbel:
3D U-Net for Brain Tumour Segmentation. 254-266 - Hao-Yu Yang, Junlin Yang:
Automatic Brain Tumor Segmentation with Contour Aware Residual Network and Adversarial Training. 267-278 - Xue Feng, Nicholas J. Tustison, Craig H. Meyer:
Brain Tumor Segmentation Using an Ensemble of 3D U-Nets and Overall Survival Prediction Using Radiomic Features. 279-288 - Amir Gholami, Shashank Subramanian, Varun Shenoy, Naveen Himthani, Xiangyu Yue, Sicheng Zhao, Peter H. Jin, George Biros, Kurt Keutzer:
A Novel Domain Adaptation Framework for Medical Image Segmentation. 289-298 - Siddhartha Chandra, Maria Vakalopoulou, Lucas Fidon, Enzo Battistella, Théo Estienne, Roger Sun, Charlotte Robert, Eric Deutsch, Nikos Paragios:
Context Aware 3D CNNs for Brain Tumor Segmentation. 299-310 - Andriy Myronenko:
3D MRI Brain Tumor Segmentation Using Autoencoder Regularization. 311-320 - Mina Rezaei, Haojin Yang, Christoph Meinel:
voxel-GAN: Adversarial Framework for Learning Imbalanced Brain Tumor Segmentation. 321-333 - Szidónia Lefkovits, László Szilágyi, László Lefkovits:
Brain Tumor Segmentation and Survival Prediction Using a Cascade of Random Forests. 334-345 - Hongdou Yao, Xiaobing Zhou, Xuejie Zhang:
Automatic Segmentation of Brain Tumor Using 3D SE-Inception Networks with Residual Connections. 346-357 - Wei Chen, Boqiang Liu, Suting Peng, Jiawei Sun, Xu Qiao:
S3D-UNet: Separable 3D U-Net for Brain Tumor Segmentation. 358-368 - Ujjwal Baid, Sanjay N. Talbar, Swapnil Rane, Sudeep Gupta, Meenakshi H. Thakur, Aliasgar Moiyadi, Siddhesh Thakur, Abhishek Mahajan:
Deep Learning Radiomics Algorithm for Gliomas (DRAG) Model: A Novel Approach Using 3D UNET Based Deep Convolutional Neural Network for Predicting Survival in Gliomas. 369-379 - Lutao Dai, Tengfei Li, Hai Shu, Liming Zhong, Haipeng Shen, Hongtu Zhu:
Automatic Brain Tumor Segmentation with Domain Adaptation. 380-392 - Santi Puch, Irina Sánchez, Aura Hernández, Gemma Piella, Vesna Prckovska:
Global Planar Convolutions for Improved Context Aggregation in Brain Tumor Segmentation. 393-405 - Eric Nathan Carver, Chang Liu, Weiwei Zong, Zhenzhen Dai, James M. Snyder, Joon Lee, Ning Wen:
Automatic Brain Tumor Segmentation and Overall Survival Prediction Using Machine Learning Algorithms. 406-418 - Eze Benson, Michael P. Pound, Andrew P. French, Aaron S. Jackson, Tony P. Pridmore:
Deep Hourglass for Brain Tumor Segmentation. 419-428 - Yannick Suter, Alain Jungo, Michael Rebsamen, Urspeter Knecht, Evelyn Herrmann, Roland Wiest, Mauricio Reyes:
Deep Learning Versus Classical Regression for Brain Tumor Patient Survival Prediction. 429-440 - David Gering, Kay Sun, Aaron Avery, Roger Chylla, Ajeet Vivekanandan, Lisa Kohli, Haley Knapp, Brad Paschke, Brett Young-Moxon, Nik King, Thomas Mackie:
Semi-automatic Brain Tumor Segmentation by Drawing Long Axes on Multi-plane Reformat. 441-455 - Richard McKinley, Raphael Meier, Roland Wiest:
Ensembles of Densely-Connected CNNs with Label-Uncertainty for Brain Tumor Segmentation. 456-465 - Tran Anh Tuan, Tran Anh Tuan, Pham The Bao:
Brain Tumor Segmentation Using Bit-plane and UNET. 466-475 - Evan D. H. Gates, J. Gregory Pauloski, Dawid Schellingerhout, David Fuentes:
Glioma Segmentation and a Simple Accurate Model for Overall Survival Prediction. 476-484 - Avinash Kori, Mehul Soni, B. Pranjal, Mahendra Khened, Alex Varghese, Ganapathy Krishnamurthi:
Ensemble of Fully Convolutional Neural Network for Brain Tumor Segmentation from Magnetic Resonance Images. 485-496 - Chenhong Zhou, Shengcong Chen, Changxing Ding, Dacheng Tao:
Learning Contextual and Attentive Information for Brain Tumor Segmentation. 497-507 - Zeina A. Shboul, Mahbubul Alam, Lasitha Vidyaratne, Linmin Pei, Khan M. Iftekharuddin:
Glioblastoma Survival Prediction. 508-515
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 2024-11-08 20:41 CET 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:
