… machine learning approach to image segmentation, deep learning approach to image
segmentation, deep learning architectures, typical approaches for implementing deep learning …

… that a generic deep learning architecture, retrained for … masks to train a dedicated segmentation
system. Such bricks could … to perform efficiently on separate segmentation tasks, further …

… tumor segmentation and classification model using a Deep Convolutional Neural Network
that includes a multiscale approach. … classical machine learning and deep learning published …

… , among others, and numerous segmentation algorithms are found in the literature. Against
… deep learning (DL) has prompted the development of new image segmentation approaches …

… For weakly supervised learning approaches, we investigate … of deep learning in medical
image segmentation, we also collect the currently common public medical image segmentation …

… techniques have surfaced with respect to image segmentation techniques. This article
approaches these various deep learning techniques of image segmentation from an analytical …

… a segmentation-based deep-learning architecture that is designed for the detection and
segmentation of … This paper explores suitable deep-learning approaches for the surface-quality …

… Deep learning approaches have … for segmenting 2D brain tumors in MR images using deep
neural networks (DNN) and utilizing data augmentation strategies. The proposed approach (…

… a deep learning-based technique for the segmentation of leukocyte from the microscopic
blood images. Segmentation of … Our approach is based on semantic segmentation using the …

… This poses a semantic segmentation problem (Fig. 1(a)): … deep learning approach based
on the state-of-the-art fully convolutional networks (FCN) [9] to solve this semantic segmentation …