Deep Convolutional Neural Networks

During training process, in order to make every forward unit responsible to the classification, for every forward unit there are temporary fully connected layers applied to train

– Convolution layers & Pooling layers + global architecture – Training algorithm + Dropout Regularization.. • Useful

Learning from images containing multiple objects in cluttered scenes with only weak object presence/absence labels has been so far mostly limited to representing entire images

Evolution of the Dice loss in validation, for the SegTHOR dataset, in fully supervised setting, using different configuration, from no CoordConv layer to all convolutional

The target output in visual recognition ranges from image-level labels (object/image classification) [23], lo- cation and extent of objects in the form of bounding boxes

The coarse-resolution network aims to roughly segment the organ, which is used to estimate a volume of interest (VOI). After that, a high-resolution image crop

Segmentation of organs at risk in Thoracic CT images by applying sharp mask techniques with FCN followed by Conditional Random Fields(CRF) is proposed by Trullo et

Segthor19 is the competition timed to the conference IEEE ISBI 2019 that addresses the problem of organs at risk seg- mentation in Computed Tomography (CT) images1. In this paper,