Improving Joint Learning of Chest X-Ray and Radiology Report by Word Region Alignment.

Self-supervised learning provides an opportunity to explore unlabeled chest X-rays and their associated free-text reports accumulated in clinical routine without manual supervision. This paper proposes a Joint Image Text Representation Learning Network (JoImTeRNet) for pre-training on chest X-ray images and their radiology reports. The model was pre-trained on both the global image-sentence level and the local image region-word level for visual-textual matching.

Development of Individuality in Children's Handwriting.

Handwriting of children in early grades is studied from the viewpoint of quantitatively measuring the development of handwriting individuality. Handwriting samples of children, in grades 2-4, writing a paragraph of text in both handprinted and cursive, collected over a period of 3 years, were analyzed using two different approaches: (i) characteristics of the word "and" and (ii) entire paragraphs using an automated system. In the first approach, word characteristics were analyzed using statistical measures.

Fast k-nearest neighbor classification using cluster-based trees.

Most fast k-nearest neighbor (k-NN) algorithms exploit metric properties of distance measures for reducing computation cost and a few can work effectively on both metric and nonmetric measures. We propose a cluster-based tree algorithm to accelerate k-NN classification without any presuppositions about the metric form and properties of a dissimilarity measure. A mechanism of early decision making and minimal side-operations for choosing searching paths largely contribute to the efficiency of the algorithm.

Individuality of handwriting.

Motivated by several rulings in United States courts concerning expert testimony in general, and handwriting testimony in particular, we undertook a study to objectively validate the hypothesis that handwriting is individual. Handwriting samples of 1,500 individuals, representative of the U.S.