Using YOLO Network for Automatic Processing of Finite Automata Images with Application to Bit-Strings Recognition

Costa, Daniela S.; Mello, Carlos A. B.

doi:10.1145/3573128.3604898

Cited by 1 publication

(1 citation statement)

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“…It also offers coarse segmentation. It is a specialized form of YOLO (You Only Look Once) [30], used in many fields of computer vision, including the recognition of handwritten diagrams: for example, in [31], the authors focus on recognizing handwritten diagrams, particularly finite automata images, exploring the use of YOLO and YOLO-Tiny networks for symbol detection and bit-string processing, and achieving promising results with 82.04% average precision and 97.20% recall in detecting finite automata symbols in a handwritten dataset.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Techniques and Pedigree Charts in Oncogenetics: Towards an Experimental Multioutput Software System for Digitization and Risk Prediction

Conte,

Rizzo,

Grassi

et al. 2024

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Pedigree charts remain essential in oncological genetic counseling for identifying individuals with an increased risk of developing hereditary tumors. However, this valuable data source often remains confined to paper files, going unused. We propose a computer-aided detection/diagnosis system, based on machine learning and deep learning techniques, capable of the following: (1) assisting genetic oncologists in digitizing paper-based pedigree charts, and in generating new digital ones, and (2) automatically predicting the genetic predisposition risk directly from these digital pedigree charts. To the best of our knowledge, there are no similar studies in the current literature, and consequently, no utilization of software based on artificial intelligence on pedigree charts has been made public yet. By incorporating medical images and other data from omics sciences, there is also a fertile ground for training additional artificial intelligence systems, broadening the software predictive capabilities. We plan to bridge the gap between scientific advancements and practical implementation by modernizing and enhancing existing oncological genetic counseling services. This would mark the pioneering development of an AI-based application designed to enhance various aspects of genetic counseling, leading to improved patient care and advancements in the field of oncogenetics.

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Section: Introductionmentioning

confidence: 99%