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1st DART / 1st MIL3ID @ MICCAI 2019: Shenzhen, China
- Qian Wang, Fausto Milletari, Hien Van Nguyen, Shadi Albarqouni
, M. Jorge Cardoso, Nicola Rieke
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
Learning Interpretable Disentangled Representations Using Adversarial VAEs. 37-44 - Eric Kerfoot
Synthesising Images and Labels Between MR Sequence Types with CycleGAN. 45-53 - Mauricio Orbes-Arteaga
Multi-domain Adaptation in Brain MRI Through Paired Consistency and Adversarial Learning. 54-62 - Yucheng Liu, Naji Khosravan, Yulin Liu, Joseph N. Stember
Cross-Modality Knowledge Transfer for Prostate Segmentation from CT Scans. 63-71 - Feng Zhang, Yutong Xie
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
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
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
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
Semi-supervised Learning of Fetal Anatomy from Ultrasound. 157-164 - Karin van Garderen, Marion Smits
Multi-modal Segmentation with Missing MR Sequences Using Pre-trained Fusion Networks. 165-172 - Yunguan Fu
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
A Convolutional Neural Network Method for Boundary Optimization Enables Few-Shot Learning for Biomedical Image Segmentation. 190-198 - Chengliang Dai
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
A Weakly Supervised Method for Instance Segmentation of Biological Cells. 216-224 - Khalil Ouardini, Huijuan Yang
Towards Practical Unsupervised Anomaly Detection on Retinal Images. 225-234 - Mina Amiri
Fine Tuning U-Net for Ultrasound Image Segmentation: Which Layers? 235-242 - Toan Duc Bui, Li Wang
Multi-task Learning for Neonatal Brain Segmentation Using 3D Dense-Unet with Dense Attention Guided by Geodesic Distance. 243-251
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