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Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge
Arnaud Arindra Adiyoso Setio, Alberto Traverso, Thomas de Bel +29
Automatic detection of pulmonary nodules in thoracic computed tomography (CT) scans has been an active area of research for the last two decades. However, there have only been few…
VoxResNet: Deep Voxelwise Residual Networks for Volumetric Brain Segmentation
Hao Chen, Qi Dou, Lequan Yu +1
Recently deep residual learning with residual units for training very deep neural networks advanced the state-of-the-art performance on 2D image recognition tasks, e.g., object det…
Iterative Multi-domain Regularized Deep Learning for Anatomical Structure Detection and Segmentation from Ultrasound Images
Hao Chen, Yefeng Zheng, Jin-Hyeong Park +2
Accurate detection and segmentation of anatomical structures from ultrasound images are crucial for clinical diagnosis and biometric measurements. Although ultrasound imaging has b…
3D Deeply Supervised Network for Automatic Liver Segmentation from CT Volumes
Qi Dou, Hao Chen, Yueming Jin +3
Automatic liver segmentation from CT volumes is a crucial prerequisite yet challenging task for computer-aided hepatic disease diagnosis and treatment. In this paper, we present a…
DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation
Hao Chen, Xiaojuan Qi, Lequan Yu +1
The morphology of glands has been used routinely by pathologists to assess the malignancy degree of adenocarcinomas. Accurate segmentation of glands from histology images is a cruc…
Gland Segmentation in Colon Histology Images: The GlaS Challenge Contest
Korsuk Sirinukunwattana, Josien P. W. Pluim, Hao Chen +16
Colorectal adenocarcinoma originating in intestinal glandular structures is the most common form of colon cancer. In clinical practice, the morphology of intestinal glands, includi…