… This work investigates a potential solution to this problem by using segmented image data
… (CNN) models. As compared to the F-CNN model trained using full images, S-CNN model …

… In this review, the segmentation deep learning algorithms refer to … segmentation algorithms,
which are derivatives of CNNs. Compared with patch-based CNNs, segmentation deep …

… that make CNNs good for high level tasks such as classification, responses from the later
layers of deep CNNs are not sufficiently well localized for accurate object segmentation. To …

… CNN architectures for tackling brain tumor segmentation. Our … CNN architectures that both
exploit the efficiency of CNNs, … between adjacent labels in the segmentation. The idea is simple…

… of the segmented dataset … deep CNNs and bigger datasets, the better progress of CADx
for the small and not well-balanced datasets even could be obtained by better segmentation, …

… We propose Hough-CNN, a novel segmentation approach based on a voting strategy. We
show … Hough-CNN delivers results comparable or superior to other state-of-the-art approaches …

… Most of the image segmentation algorithms use this property of CNNs to somehow generate
the segmentation masks as required to solve the problem. As shown in Figure 3, the earlier …

… and adequately train deep neural models. The suggested LuNet deep CNN model for detecting
… For this task, we created an efficient CNN architecture dubbed "LuNet for medical picture …

… We presented a novel method based on an ensemble of deep CNNs to segment isointense
infant brains in multi-modal MRI images. Our fully-convolutional (FCNN) network considers …

… Then, with this unique dataset, we proposed an automatic end-to-end framework using deep
neural network for tissue-level segmentation. Moreover, we provide a deep analysis of the …