Abstract is missing.
- Noise as Domain Shift: Denoising Medical Images by Unpaired Image TranslationIlja Manakov, Markus Rohm, Christoph Kern, Benedikt Schworm, Karsten Kortuem, Volker Tresp. 3-10 [doi]
- Temporal Consistency Objectives Regularize the Learning of Disentangled RepresentationsGabriele Valvano, Agisilaos Chartsias, Andrea Leo, Sotirios A. Tsaftaris. 11-19 [doi]
- Multi-layer Domain Adaptation for Deep Convolutional NetworksOzan Ciga, Jianan Chen, Anne L. Martel. 20-27 [doi]
- Intramodality Domain Adaptation Using Self Ensembling and Adversarial TrainingZahil Shanis, Samuel Gerber, Mingchen Gao, Andinet Enquobahrie. 28-36 [doi]
- Learning Interpretable Disentangled Representations Using Adversarial VAEsMhd Hasan Sarhan, Abouzar Eslami, Nassir Navab, Shadi Albarqouni. 37-44 [doi]
- Synthesising Images and Labels Between MR Sequence Types with CycleGANEric Kerfoot, Esther Puyol-Antón, Bram Ruijsink, Rina Ariga, Ernesto Zacur, Pablo Lamata, Julia A. Schnabel. 45-53 [doi]
- Multi-domain Adaptation in Brain MRI Through Paired Consistency and Adversarial LearningMauricio 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. 54-62 [doi]
- Cross-Modality Knowledge Transfer for Prostate Segmentation from CT ScansYucheng Liu, Naji Khosravan, Yulin Liu, Joseph N. Stember, Jonathan Shoag, Ulas Bagci, Sachin Jambawalikar. 63-71 [doi]
- A Pulmonary Nodule Detection Method Based on Residual Learning and Dense ConnectionFeng Zhang, Yutong Xie, Yong Xia, Yanning Zhang. 72-80 [doi]
- Harmonization and Targeted Feature Dropout for Generalized Segmentation: Application to Multi-site Traumatic Brain Injury ImagesYilin Liu, Gregory R. Kirk, Brendon M. Nacewicz, Martin A. Styner, Mingren Shen, Dong Nie, Nagesh Adluru, Benjamin Yeske, Peter A. Ferrazzano, Andrew L. Alexander. 81-89 [doi]
- Improving Pathological Structure Segmentation via Transfer Learning Across DiseasesBarleen Kaur, Paul Lemaître, Raghav Mehta, Nazanin Mohammadi Sepahvand, Doina Precup, Douglas L. Arnold, Tal Arbel. 90-98 [doi]
- Generating Virtual Chromoendoscopic Images and Improving Detectability and Classification Performance of Endoscopic LesionsAkihiro Fukuda, Tadashi Miyamoto, Shunsuke Kamba, Kazuki Sumiyama. 99-107 [doi]
- Self-supervised Learning of Inverse Problem Solvers in Medical ImagingOrtal Senouf, Sanketh Vedula, Tomer Weiss, Alexander M. Bronstein, Oleg V. Michailovich, Michael Zibulevsky. 111-119 [doi]
- Weakly Supervised Segmentation of Vertebral Bodies with Iterative Slice-PropagationShiqi Peng, Bolin Lai, Guangyu Yao, Xiaoyun Zhang, Ya Zhang, Yan-Feng Wang, Hui Zhao. 120-128 [doi]
- A Cascade Attention Network for Liver Lesion Classification in Weakly-Labeled Multi-phase CT ImagesXiao Chen, Lanfen Lin, Hongjie Hu, Qiaowei Zhang, Yutaro Iwamoto, Xianhua Han, Yen-Wei Chen, Ruofeng Tong, Jian Wu. 129-138 [doi]
- CT Data Curation for Liver Patients: Phase Recognition in Dynamic Contrast-Enhanced CTBo Zhou, Adam P. Harrison, Jiawen Yao, Chi-Tung Cheng, Jing Xiao, Chien-Hung Liao, Le Lu 0001. 139-147 [doi]
- Active Learning Technique for Multimodal Brain Tumor Segmentation Using Limited Labeled ImagesDhruv Sharma, Zahil Shanis, Chandan K. Reddy, Samuel Gerber, Andinet Enquobahrie. 148-156 [doi]
- Semi-supervised Learning of Fetal Anatomy from UltrasoundJeremy Tan, Anselm Au, Qingjie Meng, Bernhard Kainz. 157-164 [doi]
- Multi-modal Segmentation with Missing MR Sequences Using Pre-trained Fusion NetworksKarin van Garderen, Marion Smits, Stefan Klein 0001. 165-172 [doi]
- More Unlabelled Data or Label More Data? A Study on Semi-supervised Laparoscopic Image SegmentationYunguan Fu, Maria R. Robu, Bongjin Koo, Crispin Schneider, Stijn van Laarhoven, Danail Stoyanov, Brian R. Davidson, Matthew J. Clarkson, Yipeng Hu. 173-180 [doi]
- Few-Shot Learning with Deep Triplet Networks for Brain Imaging Modality RecognitionSanti Puch, Irina Sánchez, Matt Rowe. 181-189 [doi]
- A Convolutional Neural Network Method for Boundary Optimization Enables Few-Shot Learning for Biomedical Image SegmentationErica M. Rutter, John H. Lagergren, Kevin B. Flores. 190-198 [doi]
- Transfer Learning from Partial Annotations for Whole Brain SegmentationChengliang Dai, Yuanhan Mo, Elsa D. Angelini, Yike Guo, Wenjia Bai. 199-206 [doi]
- Learning to Segment Skin Lesions from Noisy AnnotationsZahra Mirikharaji, Yiqi Yan, Ghassan Hamarneh. 207-215 [doi]
- A Weakly Supervised Method for Instance Segmentation of Biological CellsFidel A. Guerrero-Peña, Pedro D. Marrero-Fernández, Tsang Ing Ren, Alexandre Cunha. 216-224 [doi]
- Towards Practical Unsupervised Anomaly Detection on Retinal ImagesKhalil Ouardini, Huijuan Yang, Balagopal Unnikrishnan, Manon Romain, Camille Garcin, Houssam Zenati, J. Peter Campbell, Michael F. Chiang, Jayashree Kalpathy-Cramer, Vijay Chandrasekhar 0001, Pavitra Krishnaswamy, Chuan-Sheng Foo. 225-234 [doi]
- Fine Tuning U-Net for Ultrasound Image Segmentation: Which Layers?Mina Amiri, Rupert Brooks, Hassan Rivaz. 235-242 [doi]
- Multi-task Learning for Neonatal Brain Segmentation Using 3D Dense-Unet with Dense Attention Guided by Geodesic DistanceToan Duc Bui, Li Wang 0026, Jian Chen, Weili Lin, Gang Li 0001, Dinggang Shen. 243-251 [doi]