DART 2019
Noise as Domain Shift: Denoising Medical Images by Unpaired Image Translation
Temporal Consistency Objectives Regularize the Learning of Disentangled Representations
Multi-layer Domain Adaptation for Deep Convolutional Networks
Intramodality Domain Adaptation using Self Ensembling and Adversarial Training
Learning Interpretable Disentangled Representations using Adversarial VAEs
Synthesising Images and Labels Between MR Sequence Types With CycleGAN
Multi-Domain Adaptation in Brain MRI through Paired Consistency and Adversarial Learning
Cross-modality Knowledge Transfer for Prostate Segmentation from CT Scans
A Pulmonary Nodule Detection Method Based on Residual Learning and Dense Connection
Harmonization and Targeted Feature Dropout for Generalized Segmentation: Application to Multi-site Traumatic Brain Injury Images
Improving Pathological Structure Segmentation Via Transfer Learning Across Diseases
Generating Virtual Chromoendoscopic Images and Improving Detectability and Classification Performance of Endoscopic Lesions
MIL3ID 2019
Self-supervised learning of inverse problem solvers in medical imaging
Weakly Supervised Segmentation of Vertebral Bodies with Iterative Slice-propagation
A Cascade Attention Network for Liver Lesion Classification in Weakly-labeled Multi-phase CT Images
CT Data Curation for Liver Patients: Phase Recognition in Dynamic Contrast-Enhanced CT
Active Learning Technique for Multimodal Brain Tumor Segmentation using Limited Labeled Images
Semi-supervised Learning of Fetal Anatomy from Ultrasound
Multi-modal segmentation with missing MR sequences using pre-trained fusion networks
More unlabelled data or label more data? A study on semi-supervised laparoscopic image segmentation
Few-shot Learning with Deep Triplet Networks for Brain Imaging Modality Recognition
A Convolutional Neural Network Method for Boundary Optimization Enables Few-Shot Learning for Biomedical Ima ge Segmentation
Transfer Learning from Partial Annotations for Whole Brain Segmentation
Learning to Segment Skin Lesions from Noisy Annotations
A Weakly Supervised Method for Instance Segmentation of Biological Cells
Towards Practical Unsupervised Anomaly Detection on Retinal Images
Fine tuning U-Net for ultrasound image segmentation: which layers
Multi-task Learning for Neonatal Brain Segmentation Using 3D Dense-Unet with Dense Attention Guided by Geodesic Distance.