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2023

Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 8th International Workshop, BrainLes 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Revised Selected PapersSpyridon Bakas, Alessandro Crimi, Ujjwal Baid, Sylwia Malec, Monika Pytlarz, Bhakti Baheti, Maximilian Zenk, Reuben Dorent, editors, Volume 13769 of Lecture Notes in Computer Science, Springer, 2023. [doi]

Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 8th International Workshop, BrainLes 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Revised Selected Papers, Part IISpyridon Bakas, Alessandro Crimi, Ujjwal Baid, Sylwia Malec, Monika Pytlarz, Bhakti Baheti, Maximilian Zenk, Reuben Dorent, editors, Volume 14092 of Lecture Notes in Computer Science, Springer, 2023. [doi]

2022

3D Inception-Based TransMorph: Pre- and Post-operative Multi-contrast MRI Registration in Brain TumorsJavid Abderezaei, Aymeric Pionteck, Agamdeep Chopra, Mehmet Kurt. brainles-ws 2023: 35-45 [doi]

Leveraging 2D Deep Learning ImageNet-trained Models for Native 3D Medical Image AnalysisBhakti Baheti, Sarthak Pati, Bjoern H. Menze, Spyridon Bakas. brainles-ws 2023: 68-79 [doi]

An Efficient Cascade of U-Net-Like Convolutional Neural Networks Devoted to Brain Tumor SegmentationPhilippe Bouchet, Jean-Baptiste Deloges, Hugo Canton-Bacara, Gaëtan Pusel, Lucas Pinot, Othman Elbaz, Nicolas Boutry. brainles-ws 2023: 149-161 [doi]

Iterative Method to Register Longitudinal MRI Acquisitions in Neurosurgical ContextLuca Canalini, Jan Klein 0001, Annika Gerken, Stefan Heldmann, Alessa Hering, Horst K. Hahn. brainles-ws 2023: 262-272 [doi]

Multi-modal Transformer for Brain Tumor SegmentationJihoon Cho, Jinah Park. brainles-ws 2023: 138-148 [doi]

Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 7th International Workshop, BrainLes 2021, Held in Conjunction with MICCAI 2021, Virtual Event, September 27, 2021, Revised Selected Papers, Part IAlessandro Crimi, Spyridon Bakas, editors, Volume 12962 of Lecture Notes in Computer Science, Springer, 2022. [doi]

Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 7th International Workshop, BrainLes 2021, Held in Conjunction with MICCAI 2021, Virtual Event, September 27, 2021, Revised Selected Papers, Part IIAlessandro Crimi, Spyridon Bakas, editors, Volume 12963 of Lecture Notes in Computer Science, Springer, 2022. [doi]

MidFusNet: Mid-dense Fusion Network for Multi-modal Brain MRI SegmentationWenting Duan, Lei Zhang 0043, Jordan Colman, Giosue Gulli, Xujiong Ye. brainles-ws 2023: 102-114 [doi]

Tuning U-Net for Brain Tumor SegmentationMichal Futrega, Michal Marcinkiewicz, Pablo Ribalta. brainles-ws 2023: 162-173 [doi]

Transformer Based Models for Unsupervised Anomaly Segmentation in Brain MR ImagesAhmed Ghorbel, Ahmed Aldahdooh, Shadi Albarqouni, Wassim Hamidouche. brainles-ws 2023: 25-44 [doi]

Employing ConvexAdam for BraTS-RegChristoph Großbröhmer, Hanna Siebert, Lasse Hansen, Mattias P. Heinrich. brainles-ws 2023: 252-261 [doi]

Unsupervised Cross-Modality Domain Adaptation for Vestibular Schwannoma Segmentation and Koos Grade Prediction Based on Semi-supervised Contrastive LearningLuyi Han, Yunzhi Huang, Tao Tan, Ritse Mann. brainles-ws 2023: 49-58 [doi]

Weakly Unsupervised Domain Adaptation for Vestibular Schwannoma SegmentationShahad Hardan, Hussain Alasmawi, Xiangjian Hou, Mohammad Yaqub. brainles-ws 2023: 90-99 [doi]

Diffraction Block in Extended nn-UNet for Brain Tumor SegmentationQingfan Hou, Zhuofei Wang, Jiao Wang, Jian Jiang, Yanjun Peng. brainles-ws 2023: 174-185 [doi]

Efficient Federated Tumor Segmentation via Parameter Distance Weighted Aggregation and Client PruningMeirui Jiang, Hongzheng Yang, Xiaofan Zhang, Shaoting Zhang 0001, Qi Dou 0001. brainles-ws 2023: 161-172 [doi]

Multi-view Cross-Modality MR Image Translation for Vestibular Schwannoma and Cochlea SegmentationBogyeong Kang, Hyeonyeong Nam, Ji-Wung Han, Keun-Soo Heo, Tae-Eui Kam. brainles-ws 2023: 100-108 [doi]

Regularized Weight Aggregation in Networked Federated Learning for Glioblastoma SegmentationMuhammad Irfan Khan, Mohammad Ayyaz Azeem, Esa Alhoniemi, Elina Kontio, Suleiman A. Khan, Mojtaba Jafaritadi. brainles-ws 2023: 121-132 [doi]

Robustifying Automatic Assessment of Brain Tumor Progression from MRIKrzysztof Kotowski, Bartosz Machura, Jakub Nalepa. brainles-ws 2023: 90-101 [doi]

Federated Evaluation of nnU-Nets Enhanced with Domain Knowledge for Brain Tumor SegmentationKrzysztof Kotowski, Szymon Adamski, Bartosz Machura, Wojciech Malara, Lukasz Zarudzki, Jakub Nalepa. brainles-ws 2023: 218-227 [doi]

Infusing Domain Knowledge into nnU-Nets for Segmenting Brain Tumors in MRIKrzysztof Kotowski, Szymon Adamski, Bartosz Machura, Lukasz Zarudzki, Jakub Nalepa. brainles-ws 2023: 186-194 [doi]

An UNet-Based Brain Tumor Segmentation Framework via Optimal Mass Transportation Pre-processingJia-Wei Liao, Tsung-Ming Huang, Tiexiang Li, Wen-Wei Lin, Han Wang, Shing-Tung Yau. brainles-ws 2023: 216-228 [doi]

Enhancing Data Diversity for Self-training Based Unsupervised Cross-Modality Vestibular Schwannoma and Cochlea SegmentationHan Liu, Yubo Fan, Ipek Oguz, Benoit M. Dawant. brainles-ws 2023: 109-118 [doi]

Semi-supervised Intracranial Aneurysm Segmentation with Selected Unlabeled DataShiyu Lu, Hao Wang, Chuyang Ye. brainles-ws 2023: 115-123 [doi]

Unsupervised Anomaly Localization with Structural Feature-AutoencodersFelix Meissen, Johannes C. Paetzold, Georgios Kaissis, Daniel Rueckert. brainles-ws 2023: 14-24 [doi]

Brain Tumor Sequence Registration with Non-iterative Coarse-To-Fine Networks and Dual Deep SupervisionMingyuan Meng, Lei Bi 0001, David Feng 0003, Jinman Kim. brainles-ws 2023: 273-282 [doi]

Multi-modal Brain Tumour Segmentation Using Transformer with Optimal Patch SizeRamtin Mojtahedi, Mohammad Hamghalam, Amber L. Simpson. brainles-ws 2023: 195-204 [doi]

Robust Image Registration with Absent Correspondences in Pre-operative and Follow-Up Brain MRI Scans of Diffuse Glioma PatientsTony C. W. Mok, Albert C. S. Chung. brainles-ws 2023: 231-240 [doi]

FedPIDAvg: A PID Controller Inspired Aggregation Method for Federated LearningLeon Mächler, Ivan Ezhov, Suprosanna Shit, Johannes C. Paetzold. brainles-ws 2023: 209-217 [doi]

WSSAMNet: Weakly Supervised Semantic Attentive Medical Image Registration NetworkSahar Almahfouz Nasser, Nikhil Cherian Kurian, Mohit Meena, Saqib Shamsi, Amit Sethi. brainles-ws 2023: 15-24 [doi]

Weighting Schemes for Federated Learning in Heterogeneous and Imbalanced Segmentation DatasetsSebastian Otálora, Jonathan Rafael-Patino, Antoine Madrona, Elda Fischi-Gomez, Veronica Ravano, Tobias Kober, Søren Christensen, Arsany Hakim, Roland Wiest, Jonas Richiardi, Richard McKinley. brainles-ws 2023: 45-56 [doi]

Hybrid Window Attention Based Transformer Architecture for Brain Tumor SegmentationHimashi Peiris, Munawar Hayat, Zhaolin Chen, Gary F. Egan, Mehrtash Harandi. brainles-ws 2023: 173-182 [doi]

Robust Learning Protocol for Federated Tumor Segmentation ChallengeAmbrish Rawat, Giulio Zizzo, Swanand Kadhe, Jonathan P. Epperlein, Stefano Braghin. brainles-ws 2023: 183-195 [doi]

Ensemble Outperforms Single Models in Brain Tumor SegmentationJianxun Ren, Wei Zhang, Ning An, Qingyu Hu, Youjia Zhang, Ying Zhou. brainles-ws 2023: 142-153 [doi]

Brain Tumor Segmentation Using Neural Ordinary Differential Equations with UNet-Context Encoding NetworkMd. Shibly Sadique, Md Monibor Rahman, Walia Farzana, Ahmed G. Temtam, Khan M. Iftekharuddin. brainles-ws 2023: 205-215 [doi]

Experimenting FedML and NVFLARE for Federated Tumor Segmentation ChallengeYaying Shi, Hongjian Gao, Salman Avestimehr, Yonghong Yan 0001. brainles-ws 2023: 228-240 [doi]

A Local Score Strategy for Weight Aggregation in Federated LearningGaurav Singh. brainles-ws 2023: 133-141 [doi]

FeTS Challenge 2022 Task 1: Implementing FedMGDA + and a New PartitioningVasilis Siomos, Giacomo Tarroni, Jonathan Passerat-Palmbach. brainles-ws 2023: 154-160 [doi]

Probabilistic Tissue Mapping for Tumor Segmentation and Infiltration Detection of GliomaSelene De Sutter, Wietse Geens, Matías N. Bossa, Anne-Marie Vanbinst, Johnny Duerinck, Jef Vandemeulebroucke. brainles-ws 2023: 80-89 [doi]

Temporally Adjustable Longitudinal Fluid-Attenuated Inversion Recovery MRI Estimation / Synthesis for Multiple SclerosisJueqi Wang, Derek Berger, Erin L. Mazerolle, Othman Soufan, Jacob Levman. brainles-ws 2023: 57-67 [doi]

Model Aggregation for Federated Learning Considering Non-IID and Imbalanced Data DistributionYuan Wang, Renuga Kanagavelu, Qingsong Wei, Yechao Yang, Yong Liu. brainles-ws 2023: 196-208 [doi]

Unsupervised Method for Intra-patient Registration of Brain Magnetic Resonance Images Based on Objective Function Weighting by Inverse Consistency: Contribution to the BraTS-Reg ChallengeMarek Wodzinski, Artur Jurgas, Niccolò Marini, Manfredo Atzori, Henning Müller. brainles-ws 2023: 241-251 [doi]

Applying Quadratic Penalty Method for Intensity-Based Deformable Image Registration on BraTS-Reg Challenge 2022Kewei Yan, Yonghong Yan 0001. brainles-ws 2023: 3-14 [doi]

Koos Classification of Vestibular Schwannoma via Image Translation-Based Unsupervised Cross-Modality Domain AdaptationTao Yang, Lisheng Wang. brainles-ws 2023: 59-67 [doi]

Self-supervised iRegNet for the Registration of Longitudinal Brain MRI of Diffuse Glioma PatientsRamy A. Zeineldin, Mohamed E. Karar, Franziska Mathis-Ullrich, Oliver Burgert. brainles-ws 2023: 25-34 [doi]

Multimodal CNN Networks for Brain Tumor Segmentation in MRI: A BraTS 2022 Challenge SolutionRamy A. Zeineldin, Mohamed E. Karar, Oliver Burgert, Franziska Mathis-Ullrich. brainles-ws 2023: 127-137 [doi]

MS-MT: Multi-scale Mean Teacher with Contrastive Unpaired Translation for Cross-Modality Vestibular Schwannoma and Cochlea SegmentationZiyuan Zhao, Kaixin Xu, Huai Zhe Yeo, XuLei Yang, Cuntai Guan. brainles-ws 2023: 68-78 [doi]

An Unpaired Cross-Modality Segmentation Framework Using Data Augmentation and Hybrid Convolutional Networks for Segmenting Vestibular Schwannoma and CochleaYuzhou Zhuang, Hong Liu 0005, Enmin Song, Coskun Cetinkaya, Chih-Cheng Hung. brainles-ws 2023: 79-89 [doi]

2021

Brain Tumor Segmentation with Patch-Based 3D Attention UNet from Multi-parametric MRIXue Feng 0001, Harrison Bai, Daniel Kim, Georgios Maragkos, Jan Machaj, Ryan Kellogg. brainles-ws 2022: 90-96 [doi]

Small Lesion Segmentation in Brain MRIs with Subpixel EmbeddingAlex Wong 0001, Allison Chen, Yangchao Wu, Safa Cicek, Alexandre Tiard, Byung-Woo Hong, Stefano Soatto. brainles-ws 2022: 75-87 [doi]

Comparison of MR Preprocessing Strategies and Sequences for Radiomics-Based MGMT PredictionDaniel Abler, Vincent Andrearczyk, Valentin Oreiller, Javier Barranco Garcia, Diem Vuong, Stephanie Tanadini-Lang, Matthias Guckenberger, Mauricio Reyes 0001, Adrien Depeursinge. brainles-ws 2022: 367-380 [doi]

Brain Tumor Segmentation from Multiparametric MRI Using a Multi-encoder U-Net ArchitectureSaruar Alam, Bharath Halandur, P. G. L. Porta Mana, Dorota Goplen, Arvid Lundervold, Alexander Selvikvåg Lundervold. brainles-ws 2022: 289-301 [doi]

Extending Probabilistic U-Net Using MC-Dropout to Quantify Data and Model UncertaintyIshaan Bhat, Hugo J. Kuijf. brainles-ws 2022: 555-559 [doi]

3 U-Net for Brain Tumor Segmentation: Submission to the RSNA-ASNR-MICCAI BraTS 2021 challengeSyed Talha Bukhari, Hassan Mohy-ud-Din. brainles-ws 2022: 276-288 [doi]

Automatic Brain Tumor Segmentation with a Bridge-Unet Deeply Supervised Enhanced with Downsampling Pooling Combination, Atrous Spatial Pyramid Pooling, Squeeze-and-Excitation and EvoNormAlexandre Carré, Eric Deutsch, Charlotte Robert. brainles-ws 2022: 253-266 [doi]

Meta-learning for Medical Image Segmentation Uncertainty QuantificationSabri Can Cetindag, Mert Yergin, Deniz Alis, Ilkay Öksüz. brainles-ws 2022: 578-584 [doi]

3D CMM-Net with Deeper Encoder for Semantic Segmentation of Brain Tumors in BraTS2021 ChallengeYoonseok Choi, Mohammed A. Al-masni, Dong-hyun Kim. brainles-ws 2022: 333-343 [doi]

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 IAlessandro Crimi, Spyridon Bakas, editors, Volume 12658 of Lecture Notes in Computer Science, Springer, 2021. [doi]

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 IIAlessandro Crimi, Spyridon Bakas, editors, Volume 12659 of Lecture Notes in Computer Science, Springer, 2021. [doi]

Brain Tumor Segmentation Using Non-local Mask R-CNN and Single Model EnsembleZhenzhen Dai, Ning Wen, Eric Nathan Carver. brainles-ws 2022: 239-248 [doi]

Residual 3D U-Net with Localization for Brain Tumor SegmentationMarc Demoustier, Ines Khemir, Quoc Duong Nguyen, Lucien Martin-Gaffé, Nicolas Boutry. brainles-ws 2022: 389-399 [doi]

Combining CNNs with Transformer for Multimodal 3D MRI Brain Tumor SegmentationMariia Dobko, Danylo-Ivan Kolinko, Ostap Viniavskyi, Yurii Yelisieiev. brainles-ws 2022: 232-241 [doi]

BRATS2021: Exploring Each Sequence in Multi-modal Input for Baseline U-net PerformancePolina Druzhinina, Ekaterina Kondrateva, Arseny Bozhenko, Vyacheslav Yarkin, Maxim Sharaev, Anvar Kurmukov. brainles-ws 2022: 194-203 [doi]

A Deep Learning Approach to Glioblastoma Radiogenomic Classification Using Brain MRIAleksandr Emchinov. brainles-ws 2022: 345-356 [doi]

Radiogenomic Prediction of MGMT Using Deep Learning with Bayesian Optimized HyperparametersWalia Farzana, Ahmed G. Temtam, Zeina A. Shboul, Md Monibor Rahman, M. Shibly Sadique, Khan M. Iftekharuddin. brainles-ws 2022: 357-366 [doi]

3D MRI Brain Tumour Segmentation with Autoencoder Regularization and Hausdorff Distance Loss FunctionVladimir S. Fonov, Pedro Rosa-Neto, D. Louis Collins. brainles-ws 2022: 324-332 [doi]

Optimized U-Net for Brain Tumor SegmentationMichal Futrega, Alexandre Milesi, Michal Marcinkiewicz, Pablo Ribalta. brainles-ws 2022: 15-29 [doi]

Brain Tumor Segmentation (BraTS) Challenge Short Paper: Improving Three-Dimensional Brain Tumor Segmentation Using SegResnet and Hybrid Boundary-Dice LossCheyu Hsu, Chun-Hao Chang, Tom Weiwu Chen, Hsinhan Tsai, Shihchieh Ma, Weichung Wang. brainles-ws 2022: 334-344 [doi]

Segmenting Brain Tumors in Multi-modal MRI Scans Using a 3D SegNet ArchitectureNabil Jabareen, Soeren Lukassen. brainles-ws 2022: 377-388 [doi]

Adaptive Weight Aggregation in Federated Learning for Brain Tumor SegmentationMuhammad Irfan Khan, Mojtaba Jafaritadi, Esa Alhoniemi, Elina Kontio, Suleiman A. Khan. brainles-ws 2022: 455-469 [doi]

A Video Data Based Transfer Learning Approach for Classification of MGMT Status in Brain Tumor MR ImagesDaniel M. Lang, Jan C. Peeken, Stephanie E. Combs, Jan J. Wilkens, Stefan Bartzsch. brainles-ws 2022: 306-314 [doi]

A Two-Phase Optimal Mass Transportation Technique for 3D Brain Tumor Detection and SegmentationWen-Wei Lin, Tiexiang Li, Tsung-Ming Huang, Jia-Wei Lin, Mei-Heng Yueh, Shing-Tung Yau. brainles-ws 2022: 400-409 [doi]

Center Dropout: A Simple Method for Speed and Fairness in Federated LearningAkis Linardos, Kaisar Kushibar, Karim Lekadir. brainles-ws 2022: 481-493 [doi]

Cascaded Training Pipeline for 3D Brain Tumor SegmentationMinh Sao Khue Luu, Evgeniy N. Pavlovskiy. brainles-ws 2022: 410-420 [doi]

Extending nn-UNet for Brain Tumor SegmentationHuan Minh Luu, Sung-Hong Park. brainles-ws 2022: 173-186 [doi]

Brain Tumor Segmentation Using Deep InfomaxJitendra Marndi, Cailyn Craven, Geena Kim. brainles-ws 2022: 242-252 [doi]

Brain Tumor Segmentation Using Neural Network Topology SearchAlexandre Milesi, Michal Futrega, Michal Marcinkiewicz, Pablo Ribalta. brainles-ws 2022: 366-376 [doi]

Multi Modal Fusion for Radiogenomics Classification of Brain TumorTimothy Sum Hon Mun, Simon J. Doran, Paul Huang, Christina Messiou, Matthew D. Blackledge. brainles-ws 2022: 344-355 [doi]

Federated Learning for Brain Tumor Segmentation Using MRI and TransformersSahil S. Nalawade, Chandan Ganesh, Benjamin C. Wagner, Divya Reddy, Yudhajit Das, Fang F. Yu, Baowei Fei, Ananth J. Madhuranthakam, Joseph A. Maldjian. brainles-ws 2022: 444-454 [doi]

Holistic Network for Quantifying Uncertainties in Medical ImagesJimut Bahan Pal. brainles-ws 2022: 560-569 [doi]

Orthogonal-Nets: A Large Ensemble of 2D Neural Networks for 3D Brain Tumor SegmentationKamlesh Pawar, Shenjun Zhong, Dilshan Sasanka Goonatillake, Gary F. Egan, Zhaolin Chen. brainles-ws 2022: 54-67 [doi]

Brain Tumor Segmentation Using Two-Stage Convolutional Neural Network for Federated EvaluationKamlesh Pawar, Shenjun Zhong, Zhaolin Chen, Gary F. Egan. brainles-ws 2022: 494-505 [doi]

Unsupervised Multimodal Supervoxel Merging Towards Brain Tumor SegmentationGuillaume Pelluet, Mira Rizkallah, Oscar Acosta, Diana Mateus. brainles-ws 2022: 88-99 [doi]

Brain Tumor Segmentation with Self-supervised Enhance Region Post-processingSergey Pnev, Vladimir Groza, Bair Tuchinov, Evgeniya Amelina, Evgeniy Pavlovskiy, Nikolay Tolstokulakov, Mihail Amelin, Sergey Golushko, Andrey Letyagin. brainles-ws 2022: 267-275 [doi]

Prediction of MGMT Methylation Status of Glioblastoma Using Radiomics and Latent Space Shape FeaturesSveinn Pálsson, Stefano Cerri, Koen Van Leemput. brainles-ws 2022: 222-231 [doi]

Brain Tumor Segmentation Using UNet-Context Encoding NetworkMd Monibor Rahman, Md. Shibly Sadique, Ahmed Temtam, Walia Farzana, L. Vidyaratne, Khan M. Iftekharuddin. brainles-ws 2022: 463-472 [doi]

EMSViT: Efficient Multi Scale Vision Transformer for Biomedical Image SegmentationAbhinav Sagar. brainles-ws 2022: 39-51 [doi]

Neural Network Based Brain Tumor SegmentationDarshat Shah, Avishek Biswas, Pranali Sonpatki, Sunder Chakravarty, Nameeta Shah. brainles-ws 2022: 324-333 [doi]

A Study on Criteria for Training Collaborator Selection in Federated LearningVishruth Shambhat, Akansh Maurya, Shubham Subhas Danannavar, Rohit Kalla, Vikas Kumar Anand, Ganapathy Krishnamurthi. brainles-ws 2022: 470-480 [doi]

Brain Tumor Segmentation Using Attention Activated U-Net with Positive MiningHar Shwinder Singh. brainles-ws 2022: 431-440 [doi]

Multimodal Brain Tumor Segmentation Using Modified UNet ArchitectureGaurav Singh, Ashish Phophalia. brainles-ws 2022: 295-305 [doi]

Optimization of Deep Learning Based Brain Extraction in MRI for Low Resource EnvironmentsSiddhesh P. Thakur, Sarthak Pati, Ravi Panchumarthy, Deepthi Karkada, Junwen Wu, Dmitry Kurtaev, Chiharu Sako, Prashant Shah, Spyridon Bakas. brainles-ws 2022: 151-167 [doi]

Evaluating Glioma Growth Predictions as a Forward Ranking ProblemKarin A. van Garderen, Sebastian R. van der Voort, Maarten M. J. Wijnenga, Fatih Incekara, Georgios Kapsas, Renske Gahrmann, Ahmad Alafandi, Marion Smits, Stefan Klein 0001. brainles-ws 2022: 100-111 [doi]

HarDNet-BTS: A Harmonic Shortcut Network for Brain Tumor SegmentationHung-Yu Wu, Youn-Long Lin. brainles-ws 2022: 261-271 [doi]

MRI Brain Tumor Segmentation Using Deep Encoder-Decoder Convolutional Neural NetworksBenjamin B. Yan, Yujia Wei, Jaidip Manikrao M. Jagtap, Mana Moassefi, Diana V. Vera Garcia, Yashbir Singh, Sanaz Vahdati, Shahriar Faghani, Bradley J. Erickson, Gian Marco Conte. brainles-ws 2022: 80-89 [doi]

Disparity Autoencoders for Multi-class Brain Tumor SegmentationChandan Ganesh Bangalore Yogananda, Yudhajit Das, Benjamin C. Wagner, Sahil S. Nalawade, Divya Reddy, James Holcomb, Marco C. Pinho, Baowei Fei, Ananth J. Madhuranthakam, Joseph A. Maldjian. brainles-ws 2022: 116-124 [doi]

Evaluating Scale Attention Network for Automatic Brain Tumor Segmentation with Large Multi-parametric MRI DatabaseYading Yuan. brainles-ws 2022: 42-53 [doi]

Modeling Multi-annotator Uncertainty as Multi-class Segmentation ProblemMartin Zukovec, Lara Dular, Ziga Spiclin. brainles-ws 2022: 112-123 [doi]

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