Health & bioscience

We propose a novel formulation of the triplet objective function that improves metric learning without additional sample mining or overhead costs. Our approach aims to explicitly regularize the distance between the positive and negative samples in a triplet with respect to the anchor-negative distance. As an initial validation, we show that our method (called No Pairs Left Behind [NPLB])...

Objective: Demonstrate the importance of combining multiple readers' opinions, in a context-aware manner, when establishing the reference standard for validation of artificial intelligence (AI) applications for, e.g. chest radiographs. By comparing individual readers, majority vote of a panel, and panel-based discussion, we identify methods which maximize interobserver agreement and label...

Diabetic retinopathy (DR) is one of the fastest growing causes of blindness and has prompted the implementation of national screening programs. To help address the shortage of experts to grade images for signs of DR, there has been a surge of interest in artificial intelligence for DR detection. In this chapter, we will cover both historical and recent deep learning algorithms for automated DR...

Recently, EYE has published few manuscripts on artificial intelligence (AI) systems based on deep learning (DL). In ophthalmology, with the exponential growth in computational power, ocular imaging quality, and increasing capabilities, several groups have applied AI productively to interpret ocular images for diagnosis, referral management, risk stratification, and prognostication. Clinical...