We propose a depth estimation method from single-shot monocular endoscopic image using Lambertian surface translation by domain adaptation and multi-scale edge loss. employ two-step process including unpaired data estimation. The texture specular reflection on the of an organ reduce accuracy estimations. apply to remove these reflections. Then, we estimate fully convolutional network (FCN). Dur...

We propose a novel approach for automatic segmentation of anatomical structures on 3D CT images by voting from a fully convolutional network (FCN), which accomplishes an end-to-end, voxel-wise multiple-class classification to map each voxel in a CT image directly to an anatomical label. The proposed method simplifies the segmentation of the anatomical structures (including multiple organs) in a...

BACKGROUND Mannose-binding lectin (MBL) and ficolin-2 (FCN) are activators of the lectin pathway of complement and act as primary defences against infection. Single-nucleotide polymorphisms (SNPs) in the MBL2 and FCN2 genes influence the functionality of the proteins. Both proteins are capable of binding staphylococci, which are pathogens that frequently cause peritonitis in patients on continu...

In this paper we present a new method for cystoid macular edema (CME) segmentation in retinal Optical Coherence Tomography (OCT) images, using a fully convolutional neural network (FCN) and a fully connected conditional random fields (dense CRFs). As a first step, the framework trains the FCN model to extract features from retinal layers in OCT images, which exhibit CME, and then segments CME r...

Partial person re-identification (re-id) is a challenging problem, where only several partial observations (images) of people are available for matching. However, few studies have provided flexible solutions to identifying a person in an image containing arbitrary part of the body. In this paper, we propose a fast and accurate matching method to address this problem. The proposed method leverag...

In this paper, we introduce a simple, yet powerful pipeline for medical image segmentation that combines Fully Convolutional Networks (FCNs) with Fully Convolutional Residual Networks (FC-ResNets). We propose and examine a design that takes particular advantage of recent advances in the understanding of both Convolutional Neural Networks as well as ResNets. Our approach focuses upon the importa...

Translation of resting-state functional connectivity (FC) magnetic resonance imaging (rs-fMRI) applications from human to rodents has experienced growing interest, and bears a great potential in pre-clinical imaging as it enables assessing non-invasively the topological organization of complex FC networks (FCNs) in rodent models under normal and various pathophysiological conditions. However, t...

Speech enhancement model is used to map a noisy speech to a clean speech. In the training stage, an objective function is often adopted to optimize the model parameters. However, in most studies, there is an inconsistency between the model optimization criterion and the evaluation criterion on the enhanced speech. For example, in measuring speech intelligibility, most of the evaluation metric i...

Foreground (FG) pixel labeling plays a vital role in video surveillance. Recent engineering solutions have attempted to exploit the efficacy of deep learning (DL) models initially targeted for image classification to deal with FG pixel labeling. One major drawback of such strategy is the lacking delineation of visual objects when training samples are limited. To grapple with this issue, we intr...

In this paper, we present a general computational and operational framework for the Fuzzy Cognitive Network (FCN), which is a direct extension of Fuzzy Cognitive Maps (FCM). The proposed framework assumes a network operation, which continuously receives feedback from the system it describes and outputs control or decision values. This way, its knowledge is continuously updated making it suitabl...