70 research outputs found
Semi-Supervised Self-Taught Deep Learning for Finger Bones Segmentation
Segmentation stands at the forefront of many high-level vision tasks. In this
study, we focus on segmenting finger bones within a newly introduced
semi-supervised self-taught deep learning framework which consists of a student
network and a stand-alone teacher module. The whole system is boosted in a
life-long learning manner wherein each step the teacher module provides a
refinement for the student network to learn with newly unlabeled data.
Experimental results demonstrate the superiority of the proposed method over
conventional supervised deep learning methods.Comment: IEEE BHI 2019 accepte
MS-MT: Multi-Scale Mean Teacher with Contrastive Unpaired Translation for Cross-Modality Vestibular Schwannoma and Cochlea Segmentation
Domain shift has been a long-standing issue for medical image segmentation.
Recently, unsupervised domain adaptation (UDA) methods have achieved promising
cross-modality segmentation performance by distilling knowledge from a
label-rich source domain to a target domain without labels. In this work, we
propose a multi-scale self-ensembling based UDA framework for automatic
segmentation of two key brain structures i.e., Vestibular Schwannoma (VS) and
Cochlea on high-resolution T2 images. First, a segmentation-enhanced
contrastive unpaired image translation module is designed for image-level
domain adaptation from source T1 to target T2. Next, multi-scale deep
supervision and consistency regularization are introduced to a mean teacher
network for self-ensemble learning to further close the domain gap.
Furthermore, self-training and intensity augmentation techniques are utilized
to mitigate label scarcity and boost cross-modality segmentation performance.
Our method demonstrates promising segmentation performance with a mean Dice
score of 83.8% and 81.4% and an average asymmetric surface distance (ASSD) of
0.55 mm and 0.26 mm for the VS and Cochlea, respectively in the validation
phase of the crossMoDA 2022 challenge.Comment: Accepted by BrainLes MICCAI proceedings (5th solution for MICCAI 2022
Cross-Modality Domain Adaptation (crossMoDA) Challenge
Multi-target deep neural networks: Theoretical analysis and implementation
Singapore-China NRF-NSFC Gran
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