Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data: First Miccai Workshop, Dart 2019, and Fi

ISBN-13: 9783030333904 / Angielski / Miękka / 2019 / 254 str.

Qian Wang; Fausto Milletari; Hien V. Nguyen

Spis treści

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 Image 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.