Reading words versus seeing font or handwriting style: a study of hemifield processing.

Tachistoscopic studies have established a right field advantage for the perception of visually presented words, which has been interpreted as reflecting a left hemispheric specialization. However, it is not clear whether this is driven by the linguistic task of word processing, or also occurs when processing properties such as the style and regularity of text. We had 23 subjects perform a tachistoscopic study while they viewed five-letter words in either computer font or handwriting.

Artificial intelligence, explainability, and the scientific method: A proof-of-concept study on novel retinal biomarker discovery.

We present a structured approach to combine explainability of artificial intelligence (AI) with the scientific method for scientific discovery. We demonstrate the utility of this approach in a proof-of-concept study where we uncover biomarkers from a convolutional neural network (CNN) model trained to classify patient sex in retinal images. This is a trait that is not currently recognized by diagnosticians in retinal images, yet, one successfully classified by CNNs.

Learning from small data: Classifying sex from retinal images via deep learning.

Deep learning (DL) techniques have seen tremendous interest in medical imaging, particularly in the use of convolutional neural networks (CNNs) for the development of automated diagnostic tools. The facility of its non-invasive acquisition makes retinal fundus imaging particularly amenable to such automated approaches. Recent work in the analysis of fundus images using CNNs relies on access to massive datasets for training and validation, composed of hundreds of thousands of images.