{'fraction_non_alphanumeric': 0.016648168701442843, 'fraction_numerical': 0.0, 'mean_word_length': 5.80377358490566, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Fusing eye movements and observer narratives for expert-driven image-region annotations

Human image understanding is reflected by individuals' visual and linguistic behaviors, but the meaningful computational integration and interpretation of their multimodal representations remain a challenge. In this paper, we expand a framework for capturing image-region annotations in dermatology, a domain in which interpreting an image is influenced by experts' visual perception skills, conceptual domain knowledge, and task-oriented goals. Our work explores the hypothesis that eye movements can help us understand experts' perceptual processes and that spoken language descriptions can reveal conceptual elements of image inspection tasks. We cast the problem of meaningfully integrating visual and linguistic data as unsupervised bitext alignment. Using alignment, we create meaningful mappings between physicians' eye movements, which reveal key areas of images, and spoken descriptions of those images. The resulting alignments are then used to annotate image regions with medical concept labels. Our alignment accuracy exceeds baselines using both exact and delayed temporal correspondence. Additionally, comparison of alignment accuracy between a method that identifies clusters in the images based on eye movement vs. a method that identifies clusters using image features suggests that the two approaches perform well on different types of images and concept labels. This suggests that an image annotation framework should integrate information from more than one technique to handle heterogeneous images. We also investigate the performance of the proposed aligner for dermatological primary morphology concept labels, as well as for lesion size or type and distribution-based categories of images.