Machine learning (ML) has revolutionized medical image-based diagnostics. In this review,
we cover a rapidly emerging field that can be potentially significantly impacted by ML–eye …

We introduce eCLIP, an enhanced version of the CLIP model that integrates expert
annotations in the form of radiologist eye-gaze heatmaps. It tackles key challenges in …

Neonatal resuscitations demand an exceptional level of attentiveness from providers who
must process multiple streams of information simultaneously. Gaze strongly influences …

Pediatric respiratory disease diagnosis and subsequent treatment require accurate and
interpretable analysis. A chest X-ray is the most cost-effective and rapid method for …

Human attention modelling has proven, in recent years, to be particularly useful not only for
understanding the cognitive processes underlying visual exploration, but also for providing …

We introduce a new method called the Gaze Scanpath Transformer for predicting a search
target category during a visual search task. Previous methods for estimating visual search …

Recent studies based on chest X-ray images have shown that pneumonia can be effectively
detected using deep convolutional neural network methods. However, these methods tend …

Accurate detection of vulvovaginal candidiasis is critical for women's health, yet its sparse
distribution and visually ambiguous characteristics pose significant challenges for accurate …

Recently, integrating eye-tracking techniques and texts into disease classification has
gained traction. To address the unmet needs such as heterogeneous data alignment …

Abstract Convolutional Neural Networks (CNNs) have shown remarkable capabilities in
extracting local features from images, yet they often overlook the underlying relationships …