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Fully Convolutional Network-based Multi-Task Learning for Rectum and Rectal Cancer Segmentation
In this study, we present a fully automatic method to segment both rectu...
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Prostate Segmentation from Ultrasound Images using Residual Fully Convolutional Network
Medical imaging based prostate cancer diagnosis procedure uses intra-ope...
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Multi-Task Learning for Left Atrial Segmentation on GE-MRI
Segmentation of the left atrium (LA) is crucial for assessing its anatom...
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Uncertainty in multitask learning: joint representations for probabilistic MR-only radiotherapy planning
Multi-task neural network architectures provide a mechanism that jointly...
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A deep learning based multiscale approach to segment cancer area in liver whole slide image
This paper addresses the problem of liver cancer segmentation in Whole S...
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Bayesian Spatial Models for Voxel-wise Prostate Cancer Classification Using Multi-parametric MRI Data
Multi-parametric magnetic resonance imaging (mpMRI) plays an increasingl...
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Automatic Polyp Segmentation using Fully Convolutional Neural Network
Colorectal cancer is one of fatal cancer worldwide. Colonoscopy is the s...
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Multi-Task Learning with a Fully Convolutional Network for Rectum and Rectal Cancer Segmentation
In a rectal cancer treatment planning, the location of rectum and rectal cancer plays an important role. The aim of this study is to propose a fully automatic method to segment both rectum and rectal cancer with axial T2-weighted magnetic resonance images. We present a fully convolutional network for multi-task learning to segment both rectum and rectal cancer. Moreover, we propose an assessment method based on bias-variance decomposition to visualize and measure the regional model robustness of a segmentation network. In addition, we suggest a novel augmentation method which can improve the segmentation performance and reduce the training time. Our proposed method not only is computationally efficient due to its fully convolutional nature but also outperforms the current state-of-the-art in rectal cancer segmentation. It also shows high accuracy in rectum segmentation, for which no previous studies exist. We conclude that rectum information benefits the training of rectal cancer segmentation model, especially concerning model variance.
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