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Total 22265 results found since Jan 2013.
High User Acceptance of a Retina e-Learning App in Times of Increasing Digitalization of Medical Training for Ophthalmologists
Conclusions: The COVID-19 pandemic has led to a significant change in training behaviour in ophthalmology towards e-learning and online courses, which has not been accompanied by a general decline in training activity. The exemplarily investigated application showed a high user acceptance among ophthalmologists.Ophthalmologica
Source: Ophthalmologica - May 2, 2022 Category: Opthalmology Source Type: research
Automatic Anisotropic Diffusion Filtering and Graph-search Segmentation of Macular Spectral-domain Optical Coherence Tomographic (SD-OCT) Images.
CONCLUSION: Segmentation was evaluated using manually segmented B-scan by an Ophthalmologist as ground truth and accuracy was found to be 99.14 ± 0.27%.
PMID: 31989882 [PubMed - in process]
Source: Current Medical Imaging Reviews - January 30, 2020 Category: Radiology Tags: Curr Med Imaging Rev Source Type: research
Second Sight Medical wins expanded CMS coverage for Argus II
Second Sight Medical (NSDQ:EYES) said today its Argus II retinal prosthesis system won expanded reimbursement coverage from the Centers for Medicare and Medicaid Services in 11 additional states and the District of Columbia.
With the expansion, the Sylmar, Calif.-based company said it’s Argus II device is now reimbursable in 7 of the 12 Medicare Administrative Contractor jurisdictions, covering the device in a total of 28 states, 2 territories and D.C.
The Argus II induces visual perception in blind patients with retinitis pigmentosa by stimulating of the retina’s remaining cells with electrical pulses, which all...
Source: Mass Device - July 3, 2017 Category: Medical Devices Authors: Fink Densford Tags: Business/Financial News Optical/Ophthalmic second-sight-medical Source Type: news
Integrating Nutrition Into a Neurology Course for Osteopathic Medical Students Through Team-Based Learning
Vitamin A is fat-soluble and easy to consume in excess through supplementation; vitamin A deficiency is the leading cause of preventable blindness in lower-income countries. Medical students have minimal education on nutrition or dietary supplements. We aimed to create and test a nutrition education module for osteopathic medical students to relate practical nutrition knowledge on vitamin A supplementation and deficiency to the preclinical curriculum topic, biochemistry of the eye.
Source: Journal of Nutrition Education and Behavior - July 1, 2023 Category: Nutrition Authors: Arushi Bharel, Emily Johnston Source Type: research
Generative Adversarial Network for Medical Images (MI-GAN)
AbstractDeep learning algorithms produces state-of-the-art results for different machine learning and computer vision tasks. To perform well on a given task, these algorithms require large dataset for training. However, deep learning algorithms lack generalization and suffer from over-fitting whenever trained on small dataset, especially when one is dealing with medical images. For supervised image analysis in medical imaging, having image data along with their corresponding annotated ground-truths is costly as well as time consuming since annotations of the data is done by medical experts manually. In this paper, we propo...
Source: Journal of Medical Systems - October 12, 2018 Category: Information Technology Source Type: research
Sparse Range-Constrained Learning and Its Application for Medical Image Grading
Sparse learning has been shown to be effective in solving many real-world problems. Finding sparse representations is a fundamentally important topic in many fields of science including signal processing, computer vision, genome study, and medical imaging. One important issue in applying sparse representation is to find the basis to represent the data, especially in computer vision and medical imaging where the data are not necessary incoherent. In medical imaging, clinicians often grade the severity or measure the risk score of a disease based on images. This process is referred to as medical image grading. Manual grading...
Source: IEE Transactions on Medical Imaging - November 30, 2018 Category: Biomedical Engineering Source Type: research
CE-Net: Context Encoder Network for 2D Medical Image Segmentation
Medical image segmentation is an important step in medical image analysis. With the rapid development of a convolutional neural network in image processing, deep learning has been used for medical image segmentation, such as optic disc segmentation, blood vessel detection, lung segmentation, cell segmentation, and so on. Previously, U-net based approaches have been proposed. However, the consecutive pooling and strided convolutional operations led to the loss of some spatial information. In this paper, we propose a context encoder network (CE-Net) to capture more high-level information and preserve spatial information for ...
Source: IEE Transactions on Medical Imaging - September 30, 2019 Category: Biomedical Engineering Source Type: research
Structure and Illumination Constrained GAN for Medical Image Enhancement
The development of medical imaging techniques has greatly supported clinical decision making. However, poor imaging quality, such as non-uniform illumination or imbalanced intensity, brings challenges for automated screening, analysis and diagnosis of diseases. Previously, bi-directional GANs (e.g., CycleGAN), have been proposed to improve the quality of input images without the requirement of paired images. However, these methods focus on global appearance, without imposing constraints on structure or illumination, which are essential features for medical image interpretation. In this paper, we propose a novel and versati...
Source: IEE Transactions on Medical Imaging - December 1, 2021 Category: Biomedical Engineering Source Type: research
Second Sight Medical enters Russian market
Second Sight Medical (NSDQ:EYES) said today it entered the Russian market, with the 1st patient treated with its Argus II Retinal Prosthesis System in Moscow.
The Argus II induces visual perception in blind patients with retinitis pigmentosa by stimulating of the retina’s remaining cells with electrical pulses, which allows for the perception of light patterns to the brain, the Sylmar, Calif.-based company said.
The 1st implant was performed at the Federal State Institution & Clinical Research Center of the Federal Medico-Biological Agency at the end of June 2017 by a team of surgeons, with Dr. Paulo Stanga of the U...
Source: Mass Device - July 26, 2017 Category: Medical Devices Authors: Fink Densford Tags: Business/Financial News Optical/Ophthalmic second-sight-medical Source Type: news
CPFNet: Context Pyramid Fusion Network for Medical Image Segmentation
Accurate and automatic segmentation of medical images is a crucial step for clinical diagnosis and analysis. The convolutional neural network (CNN) approaches based on the U-shape structure have achieved remarkable performances in many different medical image segmentation tasks. However, the context information extraction capability of single stage is insufficient in this structure, due to the problems such as imbalanced class and blurred boundary. In this paper, we propose a novel Context Pyramid Fusion Network (named CPFNet) by combining two pyramidal modules to fuse global/multi-scale context information. Based on the U...
Source: IEE Transactions on Medical Imaging - September 30, 2020 Category: Biomedical Engineering Source Type: research
SESV: Accurate Medical Image Segmentation by Predicting and Correcting Errors
Medical image segmentation is an essential task in computer-aided diagnosis. Despite their prevalence and success, deep convolutional neural networks (DCNNs) still need to be improved to produce accurate and robust enough segmentation results for clinical use. In this paper, we propose a novel and generic framework called Segmentation-Emendation-reSegmentation-Verification (SESV) to improve the accuracy of existing DCNNs in medical image segmentation, instead of designing a more accurate segmentation model. Our idea is to predict the segmentation errors produced by an existing model and then correct them. Since predicting ...
Source: IEE Transactions on Medical Imaging - January 1, 2021 Category: Biomedical Engineering Source Type: research
Proxy-Bridged Image Reconstruction Network for Anomaly Detection in Medical Images
Anomaly detection in medical images refers to the identification of abnormal images with only normal images in the training set. Most existing methods solve this problem with a self-reconstruction framework, which tends to learn an identity mapping and reduces the sensitivity to anomalies. To mitigate this problem, in this paper, we propose a novel Proxy-bridged Image Reconstruction Network (ProxyAno) for anomaly detection in medical images. Specifically, we use an intermediate proxy to bridge the input image and the reconstructed image. We study different proxy types, and we find that the superpixel-image (SI) is the best...
Source: IEE Transactions on Medical Imaging - March 1, 2022 Category: Biomedical Engineering Source Type: research
Improving Medical Images Classification With Label Noise Using Dual-Uncertainty Estimation
Deep neural networks are known to be data-driven and label noise can have a marked impact on model performance. Recent studies have shown great robustness to classic image recognition even under a high noisy rate. In medical applications, learning from datasets with label noise is more challenging since medical imaging datasets tend to have instance-dependent noise (IDN) and suffer from high observer variability. In this paper, we systematically discuss the two common types of label noise in medical images - disagreement label noise from inconsistency expert opinions and single-target label noise from biased aggregation of...
Source: IEE Transactions on Medical Imaging - June 1, 2022 Category: Biomedical Engineering Source Type: research
Data-Driven Deep Supervision for Medical Image Segmentation
Medical image segmentation plays a vital role in disease diagnosis and analysis. However, data-dependent difficulties such as low image contrast, noisy background, and complicated objects of interest render the segmentation problem challenging. These difficulties diminish dense prediction and make it tough for known approaches to explore data-specific attributes for robust feature extraction. In this paper, we study medical image segmentation by focusing on robust data-specific feature extraction to achieve improved dense prediction. We propose a new deep convolutional neural network (CNN), which exploits specific attribut...
Source: IEE Transactions on Medical Imaging - June 1, 2022 Category: Biomedical Engineering Source Type: research
MISSFormer: An Effective Transformer for 2D Medical Image Segmentation
Transformer-based methods are recently popular in vision tasks because of their capability to model global dependencies alone. However, it limits the performance of networks due to the lack of modeling local context and global-local correlations of multi-scale features. In this paper, we present MISSFormer, a Medical Image Segmentation tranSFormer. MISSFormer is a hierarchical encoder-decoder network with two appealing designs: 1) a feed-forward network in transformer block of U-shaped encoder-decoder structure is redesigned, ReMix-FFN, which explore global dependencies and local context for better feature discrimination b...
Source: IEE Transactions on Medical Imaging - May 1, 2023 Category: Biomedical Engineering Source Type: research
