default search action
5th Brainles@MICCAI 2019: Shenzhen, China - Part I
- Alessandro Crimi
, Spyridon Bakas
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 5th International Workshop, BrainLes 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Revised Selected Papers, Part I. Lecture Notes in Computer Science 11992, Springer 2020, ISBN 978-3-030-46639-8
Brain Lesion Image Analysis
- Mohammad Hamghalam
Convolutional 3D to 2D Patch Conversion for Pixel-Wise Glioma Segmentation in MRI Scans. 3-12 - Miguel Monteiro, Konstantinos Kamnitsas
TBI Lesion Segmentation in Head CT: Impact of Preprocessing and Data Augmentation. 13-22 - Mingsong Zhou, Xingce Wang, Zhongke Wu, Jose M. Pozo, Alejandro F. Frangi
Aneurysm Identification in Cerebral Models with Multiview Convolutional Neural Network. 23-31 - Po-Yu Kao
Predicting Clinical Outcome of Stroke Patients with Tractographic Feature. 32-43 - Caleb M. Grenko, Angela N. Viaene, MacLean P. Nasrallah, Michael D. Feldman, Hamed Akbari
Towards Population-Based Histologic Stain Normalization of Glioblastoma. 44-56 - Siddhesh P. Thakur, Jimit Doshi, Sarthak Pati
Skull-Stripping of Glioblastoma MRI Scans Using 3D Deep Learning. 57-68 - Christian Lucas, Linda F. Aulmann, André Kemmling, Amir Madany Mamlouk, Mattias P. Heinrich
Estimation of the Principal Ischaemic Stroke Growth Directions for Predicting Tissue Outcomes. 69-79 - Huan Wang
Global and Local Multi-scale Feature Fusion Enhancement for Brain Tumor Segmentation and Pancreas Segmentation. 80-88 - Jeroen Bertels
Optimization with Soft Dice Can Lead to a Volumetric Bias. 89-97 - Mattias Billast
Improved Inter-scanner MS Lesion Segmentation by Adversarial Training on Longitudinal Data. 98-107 - Joshua Durso-Finley, Douglas L. Arnold, Tal Arbel:
Saliency Based Deep Neural Network for Automatic Detection of Gadolinium-Enhancing Multiple Sclerosis Lesions in Brain MRI. 108-118 - Boris Shirokikh
Deep Learning for Brain Tumor Segmentation in Radiosurgery: Prospective Clinical Evaluation. 119-128
Brain Tumor Image Segmentation
- Feifan Wang
3D U-Net Based Brain Tumor Segmentation and Survival Days Prediction. 131-141 - Minglin Chen, Yaozu Wu, Jianhuang Wu:
Aggregating Multi-scale Prediction Based on 3D U-Net in Brain Tumor Segmentation. 142-152 - Mohammad Hamghalam
Brain Tumor Synthetic Segmentation in 3D Multimodal MRI Scans. 153-162 - Xiangyu Li
Multi-step Cascaded Networks for Brain Tumor Segmentation. 163-173 - Minh H. Vu, Tufve Nyholm, Tommy Löfstedt:
TuNet: End-to-End Hierarchical Brain Tumor Segmentation Using Cascaded Networks. 174-186 - Nicolas Boutry
Using Separated Inputs for Multimodal Brain Tumor Segmentation with 3D U-Net-like Architectures. 187-199 - Soopil Kim, Miguel Luna, Philip Chikontwe, Sang Hyun Park:
Two-Step U-Nets for Brain Tumor Segmentation and Random Forest with Radiomics for Survival Time Prediction. 200-209 - Yuan-Xing Zhao, Yan-Ming Zhang, Cheng-Lin Liu:
Bag of Tricks for 3D MRI Brain Tumor Segmentation. 210-220 - Mehdi Amian, Mohammadreza Soltaninejad:
Multi-resolution 3D CNN for MRI Brain Tumor Segmentation and Survival Prediction. 221-230 - Zeyu Jiang, Changxing Ding, Minfeng Liu, Dacheng Tao:
Two-Stage Cascaded U-Net: 1st Place Solution to BraTS Challenge 2019 Segmentation Task. 231-241 - Xinchao Cheng, Zongkang Jiang, Qiule Sun, Jianxin Zhang:
Memory-Efficient Cascade 3D U-Net for Brain Tumor Segmentation. 242-253 - Florian Kofler, Johannes C. Paetzold, Ivan Ezhov, Suprosanna Shit
A Baseline for Predicting Glioblastoma Patient Survival Time with Classical Statistical Models and Primitive Features Ignoring Image Information. 254-261 - Mobarakol Islam
Brain Tumor Segmentation and Survival Prediction Using 3D Attention UNet. 262-272 - Wei Shi, Enshuai Pang, Qiang Wu, Fengming Lin:
Brain Tumor Segmentation Using Dense Channels 2D U-net and Multiple Feature Extraction Network. 273-283 - M. Subin Sahayam
Brain Tumour Segmentation on MRI Images by Voxel Classification Using Neural Networks, and Patient Survival Prediction. 284-294 - Javier Juan-Albarracín
ONCOhabitats Glioma Segmentation Model. 295-303 - Xue Feng
Brain Tumor Segmentation with Uncertainty Estimation and Overall Survival Prediction. 304-314 - Dong Guo, Lu Wang, Tao Song, Guotai Wang
Cascaded Global Context Convolutional Neural Network for Brain Tumor Segmentation. 315-326 - Leon Weninger, Qianyu Liu, Dorit Merhof:
Multi-task Learning for Brain Tumor Segmentation. 327-337 - Rupal R. Agravat
Brain Tumor Segmentation and Survival Prediction. 338-348 - Sun'ao Liu, Xiaonan Guo:
Improving Brain Tumor Segmentation with Multi-direction Fusion and Fine Class Prediction. 349-358 - Kamlesh Pawar, Zhaolin Chen
An Ensemble of 2D Convolutional Neural Network for 3D Brain Tumor Segmentation. 359-367 - Sebastian Starke
An Integrative Analysis of Image Segmentation and Survival of Brain Tumour Patients. 368-378 - Richard McKinley
Triplanar Ensemble of 3D-to-2D CNNs with Label-Uncertainty for Brain Tumor Segmentation. 379-387 - Markus Frey, Matthias Nau:
Memory Efficient Brain Tumor Segmentation Using an Autoencoder-Regularized U-Net. 388-396
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 2024-10-08 20:40 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: