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Cataract is one of the leading causes of reversible visual impairment and blindness globally. Over the years, researchers have achieved significant progress in developing state-of-the-art artificial intelligence techniques for automatic cataract classification and grading, helping clinicians prevent and treat cataract in time. This paper provides a comprehensive survey of recent advances in machine learning for cataract classification and grading based on ophthalmic images. We summarize existing literature from two research directions: conventional machine learning techniques and deep learning techniques. This paper also provides insights into existing works of both merits and limitations. In addition, we discuss several challenges of automatic cataract classification and grading based on machine learning techniques and present possible solutions to these challenges for future research.
An image dataset of 10 different size molecules, where each molecule has 2,000 structural variants, is generated from the 2D cross-sectional projection of Molecular Dynamics trajectories. The purpose of this dataset is to provide a benchmark dataset
Advances in computing power, deep learning architectures, and expert labelled datasets have spurred the development of medical imaging artificial intelligence systems that rival clinical experts in a variety of scenarios. The National Institutes of H
Medical imaging is an important research field with many opportunities for improving patients health. However, there are a number of challenges that are slowing down the progress of the field as a whole, such optimizing for publication. In this paper
Brain tumor is one of the leading causes of cancer-related death globally among children and adults. Precise classification of brain tumor grade (low-grade and high-grade glioma) at early stage plays a key role in successful prognosis and treatment p
Radiographs are used as the most important imaging tool for identifying spine anomalies in clinical practice. The evaluation of spinal bone lesions, however, is a challenging task for radiologists. This work aims at developing and evaluating a deep l