1,577 research outputs found
3DQ: Compact Quantized Neural Networks for Volumetric Whole Brain Segmentation
Model architectures have been dramatically increasing in size, improving
performance at the cost of resource requirements. In this paper we propose 3DQ,
a ternary quantization method, applied for the first time to 3D Fully
Convolutional Neural Networks (F-CNNs), enabling 16x model compression while
maintaining performance on par with full precision models. We extensively
evaluate 3DQ on two datasets for the challenging task of whole brain
segmentation. Additionally, we showcase our method's ability to generalize on
two common 3D architectures, namely 3D U-Net and V-Net. Outperforming a variety
of baselines, the proposed method is capable of compressing large 3D models to
a few MBytes, alleviating the storage needs in space critical applications.Comment: Accepted to MICCAI 201
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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