23,406 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
Task Decomposition and Synchronization for Semantic Biomedical Image Segmentation
Semantic segmentation is essentially important to biomedical image analysis.
Many recent works mainly focus on integrating the Fully Convolutional Network
(FCN) architecture with sophisticated convolution implementation and deep
supervision. In this paper, we propose to decompose the single segmentation
task into three subsequent sub-tasks, including (1) pixel-wise image
segmentation, (2) prediction of the class labels of the objects within the
image, and (3) classification of the scene the image belonging to. While these
three sub-tasks are trained to optimize their individual loss functions of
different perceptual levels, we propose to let them interact by the task-task
context ensemble. Moreover, we propose a novel sync-regularization to penalize
the deviation between the outputs of the pixel-wise segmentation and the class
prediction tasks. These effective regularizations help FCN utilize context
information comprehensively and attain accurate semantic segmentation, even
though the number of the images for training may be limited in many biomedical
applications. We have successfully applied our framework to three diverse 2D/3D
medical image datasets, including Robotic Scene Segmentation Challenge 18
(ROBOT18), Brain Tumor Segmentation Challenge 18 (BRATS18), and Retinal Fundus
Glaucoma Challenge (REFUGE18). We have achieved top-tier performance in all
three challenges.Comment: IEEE Transactions on Medical Imagin
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