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Found 28 papers, 2 papers with code
Tensor Denoising via Amplification and Stable Rank Methods
Tensors in the form of multilinear arrays are ubiquitous in data science applications.
Prediction of Oral Food Challenge Outcomes via Ensemble Learning
The results of this analysis suggest that ensemble learning has the potential to predict OFC outcomes and reveal relevant clinical factors for further study.
A Novel Tropical Geometry-based Interpretable Machine Learning Method: Application in Prognosis of Advanced Heart Failure
In addition to learning humanly understandable rules from the dataset, existing fuzzy domain knowledge can be easily transferred into the network and used to facilitate model training.
Motion-based Camera Localization System in Colonoscopy Videos
In this study, we propose a camera localization system to estimate the relative location of the camera and classify the colon into anatomical segments.
Radial basis function kernel optimization for Support Vector Machine classifiers
Experimental results show that OKSVM performs better irrespective of the initial values of the RBF hyperparameter.
Gland Segmentation in Histopathology Images Using Deep Networks and Handcrafted Features
Histopathology images contain essential information for medical diagnosis and prognosis of cancerous disease.
Fetal Ultrasound Image Segmentation for Measuring Biometric Parameters Using Multi-Task Deep Learning
Ultrasound imaging is a standard examination during pregnancy that can be used for measuring specific biometric parameters towards prenatal diagnosis and estimating gestational age.
An Unsupervised Feature Learning Approach to Reduce False Alarm Rate in ICUs
In this paper, we propose a novel set of high-level features based on unsupervised feature learning technique in order to effectively capture the characteristics of different arrhythmia in electrocardiogram (ECG) signal and differentiate them from irregularity in signals due to different sources of signal disturbances.
ReDMark: Framework for Residual Diffusion Watermarking on Deep Networks
Due to the rapid growth of machine learning tools and specifically deep networks in various computer vision and image processing areas, application of Convolutional Neural Networks for watermarking have recently emerged.
Multiple Abnormality Detection for Automatic Medical Image Diagnosis Using Bifurcated Convolutional Neural Network
In recent years portable medical imaging devices such as capsule endoscopy and digital dermatoscope have been introduced and made the diagnosis procedure easier and more efficient.
Segmentation of Bleeding Regions in Wireless Capsule Endoscopy for Detection of Informative Frames
Both CNN and MLP structures are simplified to reduce the number of computational operations.
Left ventricle segmentation By modelling uncertainty in prediction of deep convolutional neural networks and adaptive thresholding inference
Deep neural networks have shown great achievements in solving complex problems.
Adaptive specular reflection detection and inpainting in colonoscopy video frames
In the detection phase, we employ both HSV and RGB color space information for segmentation of specular reflections.
Liver Segmentation in Abdominal CT Images by Adaptive 3D Region Growing
In this paper, we propose an adaptive 3D region growing with subject-specific conditions.
Liver segmentation in CT images using three dimensional to two dimensional fully convolutional network
The need for CT scan analysis is growing for pre-diagnosis and therapy of abdominal organs.
Left Ventricle Segmentation in Cardiac MR Images Using Fully Convolutional Network
In this paper we propose an automated method for segmenting the left ventricle in cardiac MR images.
Reversible Image Watermarking for Health Informatics Systems Using Distortion Compensation in Wavelet Domain
In this study a reversible watermarking is investigated with focusing on increasing the embedding capacity and reducing the distortion in medical images.
Lossless Compression of Angiogram Foreground with Visual Quality Preservation of Background
By increasing the volume of telemedicine information, the need for medical image compression has become more important.
Segmentation of Bleeding Regions in Wireless Capsule Endoscopy Images an Approach for inside Capsule Video Summarization
Wireless capsule endoscopy (WCE) is an effective means of diagnosis of gastrointestinal disorders.
Lossless Image Compression Algorithm for Wireless Capsule Endoscopy by Content-Based Classification of Image Blocks
Recent advances in capsule endoscopy systems have introduced new methods and capabilities.
Deep Learning in Pharmacogenomics: From Gene Regulation to Patient Stratification
This Perspective provides examples of current and future applications of deep learning in pharmacogenomics, including: (1) identification of novel regulatory variants located in noncoding domains and their function as applied to pharmacoepigenomics; (2) patient stratification from medical records; and (3) prediction of drugs, targets, and their interactions.
Dense Pooling layers in Fully Convolutional Network for Skin Lesion Segmentation
One of the essential tasks in medical image analysis is segmentation and accurate detection of borders.
Vessel Segmentation and Catheter Detection in X-Ray Angiograms Using Superpixels
In this framework we use our catheter detection and tracking method which detects the catheter by finding its ridge in the first frame and traces in other frames by fitting a second order polynomial on it.
Adaptive Real-Time Removal of Impulse Noise in Medical Images
In this paper a low complexity de-noising method is proposed that removes the noise by local analysis of the image blocks.
Blind Stereo Image Quality Assessment Inspired by Brain Sensory-Motor Fusion
Then, a stacked neural network model is proposed, which learns the extracted features and accurately evaluates the quality of stereo images.
Hand Gesture Recognition for Contactless Device Control in Operating Rooms
In this paper we introduce a system of hand gesture recognition based on a deep learning approach.
Extraction of Skin Lesions from Non-Dermoscopic Images Using Deep Learning
The input image is preprocessed and then its patches are fed to a convolutional neural network (CNN).
A Shapley Value Solution to Game Theoretic-based Feature Reduction in False Alarm Detection
False alarm is one of the main concerns in intensive care units and can result in care disruption, sleep deprivation, and insensitivity of care-givers to alarms.
