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ORCID1st DART / 1st MIL3ID @ MICCAI 2019: Shenzhen, China
- Qian Wang, Fausto Milletari, Hien Van Nguyen, Shadi Albarqouni
, M. Jorge Cardoso, Nicola Rieke, Ziyue Xu, Konstantinos Kamnitsas, Vishal Patel, Badri Roysam, Steve B. Jiang, S. Kevin Zhou, Khoa Luu, Ngan Le:
Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data - First MICCAI Workshop, DART 2019, and First International Workshop, MIL3ID 2019, Shenzhen, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13 and 17, 2019, Proceedings. Lecture Notes in Computer Science 11795, Springer 2019, ISBN 978-3-030-33390-4
DART 2019
- Ilja Manakov, Markus Rohm, Christoph Kern, Benedikt Schworm, Karsten U. Kortuem, Volker Tresp:
Noise as Domain Shift: Denoising Medical Images by Unpaired Image Translation. 3-10 - Gabriele Valvano, Agisilaos Chartsias, Andrea Leo, Sotirios A. Tsaftaris:
Temporal Consistency Objectives Regularize the Learning of Disentangled Representations. 11-19 - Ozan Ciga, Jianan Chen, Anne L. Martel:
Multi-layer Domain Adaptation for Deep Convolutional Networks. 20-27 - Zahil Shanis, Samuel Gerber, Mingchen Gao, Andinet Enquobahrie:
Intramodality Domain Adaptation Using Self Ensembling and Adversarial Training. 28-36 - Mhd Hasan Sarhan, Abouzar Eslami, Nassir Navab, Shadi Albarqouni:
Learning Interpretable Disentangled Representations Using Adversarial VAEs. 37-44 - Eric Kerfoot, Esther Puyol-Antón, Bram Ruijsink, Rina Ariga, Ernesto Zacur, Pablo Lamata, Julia A. Schnabel:
Synthesising Images and Labels Between MR Sequence Types with CycleGAN. 45-53 - Mauricio Orbes-Arteaga, Thomas Varsavsky, Carole H. Sudre, Zach Eaton-Rosen, Lewis J. Haddow, Lauge Sørensen, Mads Nielsen, Akshay Pai, Sébastien Ourselin, Marc Modat, Parashkev Nachev, M. Jorge Cardoso:
Multi-domain Adaptation in Brain MRI Through Paired Consistency and Adversarial Learning. 54-62 - Yucheng Liu, Naji Khosravan, Yulin Liu, Joseph N. Stember, Jonathan Shoag, Ulas Bagci, Sachin Jambawalikar:
Cross-Modality Knowledge Transfer for Prostate Segmentation from CT Scans. 63-71 - Feng Zhang, Yutong Xie, Yong Xia, Yanning Zhang:
A Pulmonary Nodule Detection Method Based on Residual Learning and Dense Connection. 72-80 - Yilin Liu, Gregory R. Kirk, Brendon M. Nacewicz, Martin A. Styner, Mingren Shen, Dong Nie, Nagesh Adluru, Benjamin Yeske, Peter A. Ferrazzano, Andrew L. Alexander:
Harmonization and Targeted Feature Dropout for Generalized Segmentation: Application to Multi-site Traumatic Brain Injury Images. 81-89 - Barleen Kaur, Paul Lemaître, Raghav Mehta, Nazanin Mohammadi Sepahvand, Doina Precup, Douglas L. Arnold, Tal Arbel:
Improving Pathological Structure Segmentation via Transfer Learning Across Diseases. 90-98 - Akihiro Fukuda, Tadashi Miyamoto, Shunsuke Kamba, Kazuki Sumiyama:
Generating Virtual Chromoendoscopic Images and Improving Detectability and Classification Performance of Endoscopic Lesions. 99-107
MIL3ID 2019
- Ortal Senouf, Sanketh Vedula, Tomer Weiss, Alexander M. Bronstein, Oleg V. Michailovich, Michael Zibulevsky:
Self-supervised Learning of Inverse Problem Solvers in Medical Imaging. 111-119 - Shiqi Peng, Bolin Lai, Guangyu Yao, Xiaoyun Zhang, Ya Zhang, Yanfeng Wang, Hui Zhao:
Weakly Supervised Segmentation of Vertebral Bodies with Iterative Slice-Propagation. 120-128 - Xiao Chen, Lanfen Lin, Hongjie Hu, Qiaowei Zhang, Yutaro Iwamoto, Xianhua Han, Yen-Wei Chen, Ruofeng Tong, Jian Wu:
A Cascade Attention Network for Liver Lesion Classification in Weakly-Labeled Multi-phase CT Images. 129-138 - Bo Zhou, Adam P. Harrison, Jiawen Yao, Chi-Tung Cheng, Jing Xiao, Chien-Hung Liao, Le Lu:
CT Data Curation for Liver Patients: Phase Recognition in Dynamic Contrast-Enhanced CT. 139-147 - Dhruv Sharma, Zahil Shanis, Chandan K. Reddy, Samuel Gerber, Andinet Enquobahrie:
Active Learning Technique for Multimodal Brain Tumor Segmentation Using Limited Labeled Images. 148-156 - Jeremy Tan, Anselm Au, Qingjie Meng, Bernhard Kainz:
Semi-supervised Learning of Fetal Anatomy from Ultrasound. 157-164 - Karin van Garderen, Marion Smits, Stefan Klein:
Multi-modal Segmentation with Missing MR Sequences Using Pre-trained Fusion Networks. 165-172 - Yunguan Fu, Maria R. Robu, Bongjin Koo, Crispin Schneider, Stijn van Laarhoven, Danail Stoyanov, Brian R. Davidson, Matthew J. Clarkson, Yipeng Hu:
More Unlabelled Data or Label More Data? A Study on Semi-supervised Laparoscopic Image Segmentation. 173-180 - Santi Puch, Irina Sánchez, Matt Rowe:
Few-Shot Learning with Deep Triplet Networks for Brain Imaging Modality Recognition. 181-189 - Erica M. Rutter, John H. Lagergren, Kevin B. Flores:
A Convolutional Neural Network Method for Boundary Optimization Enables Few-Shot Learning for Biomedical Image Segmentation. 190-198 - Chengliang Dai, Yuanhan Mo, Elsa D. Angelini, Yike Guo, Wenjia Bai:
Transfer Learning from Partial Annotations for Whole Brain Segmentation. 199-206 - Zahra Mirikharaji, Yiqi Yan, Ghassan Hamarneh:
Learning to Segment Skin Lesions from Noisy Annotations. 207-215 - Fidel A. Guerrero-Peña, Pedro D. Marrero-Fernández, Tsang Ing Ren, Alexandre Cunha:
A Weakly Supervised Method for Instance Segmentation of Biological Cells. 216-224 - Khalil Ouardini, Huijuan Yang, Balagopal Unnikrishnan, Manon Romain, Camille Garcin, Houssam Zenati, J. Peter Campbell, Michael F. Chiang, Jayashree Kalpathy-Cramer, Vijay Chandrasekhar, Pavitra Krishnaswamy, Chuan-Sheng Foo:
Towards Practical Unsupervised Anomaly Detection on Retinal Images. 225-234 - Mina Amiri, Rupert Brooks, Hassan Rivaz:
Fine Tuning U-Net for Ultrasound Image Segmentation: Which Layers? 235-242 - Toan Duc Bui, Li Wang, Jian Chen, Weili Lin, Gang Li, Dinggang Shen:
Multi-task Learning for Neonatal Brain Segmentation Using 3D Dense-Unet with Dense Attention Guided by Geodesic Distance. 243-251
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