Table of Contents

• Brain Tumor Segmentation Using Attention-Based Network in 3D MRI ImagesXiaowei Xu, Wangyuan Zhao, Jun Zhao 0010. 3-13 [doi]
• Multimodal Brain Image Segmentation and Analysis with Neuromorphic Attention-Based LearningWoo-Sup Han, Il Song Han. 14-26 [doi]
• Improving Brain Tumor Segmentation in Multi-sequence MR Images Using Cross-Sequence MR Image GenerationGuojing Zhao, Jianpeng Zhang, Yong Xia. 27-36 [doi]
• Ensemble of CNNs for Segmentation of Glioma Sub-regions with Survival PredictionSubhashis Banerjee, Harkirat Singh Arora, Sushmita Mitra. 37-49 [doi]
• Brain Tumor Segmentation Based on Attention Mechanism and Multi-model FusionXutao Guo, Chushu Yang, Ting Ma, PengZheng Zhou, Shangfeng Lu, Nan Ji, Deling Li, Tong Wang, Haiyan Lv. 50-60 [doi]
• Automatic Brain Tumour Segmentation and Biophysics-Guided Survival PredictionShuo Wang, Chengliang Dai, Yuanhan Mo, Elsa D. Angelini, Yike Guo, Wenjia Bai. 61-72 [doi]
• Multimodal Brain Tumor Segmentation and Survival Prediction Using Hybrid Machine LearningLinmin Pei, Lasitha Vidyaratne, M. Monibor Rahman, Zeina A. Shboul, Khan M. Iftekharuddin. 73-81 [doi]
• Robust Semantic Segmentation of Brain Tumor Regions from 3D MRIsAndriy Myronenko, Ali Hatamizadeh. 82-89 [doi]
• Brain Tumor Segmentation with Cascaded Deep Convolutional Neural NetworkUjjwal Baid, Nisarg A. Shah, Sanjay N. Talbar. 90-98 [doi]
• Fully Automated Brain Tumor Segmentation and Survival Prediction of Gliomas Using Deep Learning and MRIChandan Ganesh Bangalore Yogananda, Benjamin C. Wagner, Sahil S. Nalawade, Gowtham Krishnan Murugesan, Marco C. Pinho, Baowei Fei, Ananth J. Madhuranthakam, Joseph A. Maldjian. 99-112 [doi]
• 3D Automatic Brain Tumor Segmentation Using a Multiscale Input U-Net NetworkS. Rosas González, Taibou Birgui Sekou, Moncef Hidane, Clovis Tauber. 113-123 [doi]
• Semi-supervised Variational Autoencoder for Survival PredictionSveinn Pálsson, Stefano Cerri, Andrea Dittadi, Koen Van Leemput. 124-134 [doi]
• Multi-modal U-Nets with Boundary Loss and Pre-training for Brain Tumor SegmentationPablo Ribalta Lorenzo, Michal Marcinkiewicz, Jakub Nalepa. 135-147 [doi]
• Multidimensional and Multiresolution Ensemble Networks for Brain Tumor SegmentationGowtham Krishnan Murugesan, Sahil S. Nalawade, Chandan Ganesh Bangalore Yogananda, Benjamin C. Wagner, Fang F. Yu, Baowei Fei, Ananth J. Madhuranthakam, Joseph A. Maldjian. 148-157 [doi]
• Hybrid Labels for Brain Tumor SegmentationParvez Ahmad, Saqib Qamar, Seyed Raein Hashemi, LinLin Shen. 158-166 [doi]
• Two Stages CNN-Based Segmentation of Gliomas, Uncertainty Quantification and Prediction of Overall Patient SurvivalThibault Buatois, Élodie Puybareau, Guillaume Tochon, Joseph Chazalon. 167-178 [doi]
• Detection and Segmentation of Brain Tumors from MRI Using U-NetsKrzysztof Kotowski, Jakub Nalepa, Wojciech Dudzik. 179-190 [doi]
• Multimodal Segmentation with MGF-Net and the Focal Tversky Loss FunctionNabila Abraham, Naimul Mefraz Khan. 191-198 [doi]
• Brain Tumor Segmentation Using 3D Convolutional Neural NetworkKaisheng Liang, Wenlian Lu. 199-207 [doi]
• DDU-Nets: Distributed Dense Model for 3D MRI Brain Tumor SegmentationHanxiao Zhang, Jingxiong Li, Mali Shen, Yaqi Wang, Guang-Zhong Yang. 208-217 [doi]
• Brain Tumor Segmentation Based on 3D Residual U-NetMegh Bhalerao, Siddhesh Thakur. 218-225 [doi]
• Automatic Segmentation of Brain Tumor from 3D MR Images Using SegNet, U-Net, and PSP-NetYan-Ting Weng, Hsiang-Wei Chan, Teng-Yi Huang. 226-233 [doi]
• 3D Deep Residual Encoder-Decoder CNNS with Squeeze-and-Excitation for Brain Tumor SegmentationKai Yan, Qiuchang Sun, Ling Li, Zhicheng Li. 234-243 [doi]
• Overall Survival Prediction Using Conventional MRI FeaturesYanhao Ren, Pin Sun, Wenlian Lu. 244-254 [doi]
• A Multi-path Decoder Network for Brain Tumor SegmentationYunzhe Xue, Meiyan Xie, Fadi G. Farhat, Olga Boukrina, A. M. Barrett, Jeffrey R. Binder, Usman W. Roshan, William W. Graves. 255-265 [doi]
• The Tumor Mix-Up in 3D Unet for Glioma SegmentationPengyu Yin, Yingdong Hu, Jing Liu, Jiaming Duan, Wei Yang, Kun Cheng. 266-273 [doi]
• Multi-branch Learning Framework with Different Receptive Fields Ensemble for Brain Tumor SegmentationGuohua Cheng, Mengyan Luo, Linyang He, Lingqiang Mo. 274-284 [doi]
• Domain Knowledge Based Brain Tumor Segmentation and Overall Survival PredictionXiaoqing Guo, Chen Yang, Pak Lun Lam, Peter Y. M. Woo, Yixuan Yuan. 285-295 [doi]
• Encoder-Decoder Network for Brain Tumor Segmentation on Multi-sequence MRIAndrei Iantsen, Vincent Jaouen, Dimitris Visvikis, Mathieu Hatt. 296-302 [doi]
• Deep Convolutional Neural Networks for Brain Tumor Segmentation: Boosting Performance Using Deep Transfer Learning: Preliminary ResultsMostefa Ben naceur, Mohamed Akil, Rachida Saouli, Rostom Kachouri. 303-315 [doi]
• Multimodal Brain Tumor Segmentation with Normal Appearance AutoencoderMehdi Astaraki, Chunliang Wang, Gabriel Carrizo, Iuliana Toma-Dasu, Örjan Smedby. 316-323 [doi]
• Knowledge Distillation for Brain Tumor SegmentationDmitry Lachinov, Elena Shipunova, Vadim Turlapov. 324-332 [doi]
• Brain Tumor Classification Using 3D Convolutional Neural NetworkLinmin Pei, Lasitha Vidyaratne, Wei-Wen Hsu, Md Monibor Rahman, Khan M. Iftekharuddin. 335-342 [doi]
• Brain Tumor Classification with Multimodal MR and Pathology ImagesXiao Ma, Fucang Jia. 343-352 [doi]
• Automatic Classification of Brain Tumor Types with the MRI Scans and Histopathology ImagesHsiang-Wei Chan, Yan-Ting Weng, Teng-Yi Huang. 353-359 [doi]
• Brain Tumor Classification with Tumor Segmentations and a Dual Path Residual Convolutional Neural Network from MRI and Pathology ImagesYunzhe Xue, Yanan Yang, Fadi G. Farhat, Frank Y. Shih, Olga Boukrina, A. M. Barrett, Jeffrey R. Binder, William W. Graves, Usman W. Roshan. 360-367 [doi]
• From Whole Slide Tissues to Knowledge: Mapping Sub-cellular Morphology of CancerTahsin M. Kurç, Ashish Sharma 0001, Rajarsi Gupta, Le Hou, Han Le, Shahira Abousamra, Erich Bremer, Ryan Birmingham, Tammy Diprima, Nan Li, Feiqiao Wang, Joseph Balsamo, Whitney Bremer, Dimitris Samaras, Joel H. Saltz. 371-379 [doi]
• The Cancer Imaging Phenomics Toolkit (CaPTk): Technical OverviewSarthak Pati, Ashish Singh, Saima Rathore, Aimilia Gastounioti, Mark Bergman, Phuc Ngo, Sung Min Ha, Dimitrios Bounias, James Minock, Grayson Murphy, Hongming Li, Amit Bhattarai, Adam Wolf, Patmaa Sridaran, Ratheesh Kalarot, Hamed Akbari, Aristeidis Sotiras, Siddhesh P. Thakur, Ragini Verma, Russell T. Shinohara, Paul A. Yushkevich, Yong Fan, Despina Kontos, Christos Davatzikos, Spyridon Bakas. 380-394 [doi]