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 / edited by Qian Wang, Fausto Milletari, Hien V. Nguyen, Shadi Albarqouni, M. Jorge Cardoso, Nicola Rieke, Ziyue Xu, Konstantinos Kamnitsas, Vishal Patel, Badri Roysam, Steve Jiang, Kevin Zhou, Khoa Luu, Ngan Le.

Format:

Book

Contributor:

Wang, Qian, editor.

Milletari, Fausto, editor.

Nguyen, Hien V., editor.

Albarqouni, Shadi, editor.

Cardoso, M. Jorge, editor.

Rieke, Nicola, editor.

Xu, Ziyue, editor.

Kamnitsas, Konstantinos, editor.

Paṭela, Viśāla, editor.

Roysam, Badri, editor.

Jiang, Steve, editor.

Zhou, Kevin, editor.

Luu, Khoa, editor.

Lê, Ngân, editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

LNCS sublibrary. Image processing, computer vision, pattern recognition, and graphics ; SL 6, 11795.

Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 11795

Language:

English

Subjects (All):

Optical data processing.

Artificial intelligence.

Medical informatics.

Image Processing and Computer Vision.

Artificial Intelligence.

Health Informatics.

Local Subjects:

Image Processing and Computer Vision.

Artificial Intelligence.

Health Informatics.

Physical Description:

1 online resource (XVII, 254 pages) : 113 illustrations, 79 illustrations in color.

Edition:

First edition 2019.

Contained In:

Springer eBooks

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2019.

System Details:

text file PDF

Summary:

This book constitutes the refereed proceedings of the First MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2019, and the First International Workshop on Medical Image Learning with Less Labels and Imperfect Data, MIL3ID 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. DART 2019 accepted 12 papers for publication out of 18 submissions. The papers deal with methodological advancements and ideas that can improve the applicability of machine learning and deep learning approaches to clinical settings by making them robust and consistent across different domains. MIL3ID accepted 16 papers out of 43 submissions for publication, dealing with best practices in medical image learning with label scarcity and data imperfection. .

Contents:

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.

Other Format:

Printed edition:

ISBN:

978-3-030-33391-1

9783030333911

Access Restriction:

Restricted for use by site license.