Search Results for author:
Found 15 papers, 4 papers with code
OW-SLR: Overlapping Windows on Semi-Local Region for Image Super-Resolution
In this paper, we propose applying a new technique called Overlapping Windows on Semi-Local Region (OW-SLR) to an image to obtain any arbitrary resolution by taking the coordinates of the semi-local region around a point in the latent space.
Direct Estimation of Pupil Parameters Using Deep Learning for Visible Light Pupillometry
Previously we demonstrated the use of deep learning techniques to accurately detect the pupil pixels (segmentation binary mask) in case of VL images for performing VL pupillometry.
FAZSeg: A New User-Friendly Software for Quantification of the Foveal Avascular Zone
Various ocular diseases and high myopia influence the anatomical reference point Foveal Avascular Zone (FAZ) dimensions.
Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey
In the past few years, Artificial Intelligence (AI) based approaches have been used to detect and grade DR.
Rapid Classification of Glaucomatous Fundus Images
We propose a new method for training convolutional neural networks which integrates reinforcement learning along with supervised learning and use ti for transfer learning for classification of glaucoma from colored fundus images.
Uncertainty aware and explainable diagnosis of retinal disease
Deep learning methods for ophthalmic diagnosis have shown considerable success in tasks like segmentation and classification.
DenseNet for Breast Tumor Classification in Mammographic Images
Thus, the aim of this study is to build a deep convolutional neural network method for automatic detection, segmentation, and classification of breast lesions in mammography images.
Fractal Dimension and Retinal Pathology: A Meta-analysis
Due to the fractal nature of retinal blood vessels, the retinal fractal dimension is a natural parameter for researchers to explore and has garnered interest as a potential diagnostic tool.
MRI Images, Brain Lesions and Deep Learning
Medical brain image analysis is a necessary step in Computer Assisted /Aided Diagnosis (CAD) systems.
Deep Learning Based Computer-Aided Systems for Breast Cancer Imaging : A Critical Review
This paper provides a critical review of the literature on deep learning applications in breast tumor diagnosis using ultrasound and mammography images.
Quantitative and Qualitative Evaluation of Explainable Deep Learning Methods for Ophthalmic Diagnosis
The explanations from 13 different attribution methods were rated by a panel of 14 clinicians for clinical significance.
Explainable deep learning models in medical image analysis
Deep learning methods have been very effective for a variety of medical diagnostic tasks and has even beaten human experts on some of those.
OCTID: Optical Coherence Tomography Image Database
We have also included 25 normal OCT images with their corresponding ground truth delineations which can be used for an accurate evaluation of OCT image segmentation.
Application of Deep Learning in Fundus Image Processing for Ophthalmic Diagnosis -- A Review
An overview of the applications of deep learning in ophthalmic diagnosis using retinal fundus images is presented.
PixelBNN: Augmenting the PixelCNN with batch normalization and the presentation of a fast architecture for retinal vessel segmentation
Analysis of retinal fundus images is essential for eye-care physicians in the diagnosis, care and treatment of patients.
