RT Conference Proceedings
T1 Enhanced Cellular Detection Using Convolutional Neural Networks and Sliding Window Super-Resolution Inference.
A1 García Aguilar, Iván
A1 Rostyslav, Zavoiko
A1 Fernández-Rodríguez, Jose David
A1 Luque-Baena, Rafael Marcos
A1 López-Rubio, Ezequiel
K1 Redes neuronales (Informática)
K1 Cáncer - Investigación
AB Histopathology currently serves as the standard for breastcancer diagnosis, but its manual execution demands time and expertisefrom pathologists. Artificial intelligence, particularly in digital pathology, has made significant strides, offering new opportunities for precisionand efficiency in disease diagnosis. This study presents a methodology toenhance cell nuclei detection in breast cancer histopathological imagesusing convolutional neural network models to apply super-resolution andobject detection. Several model architectures are explored, and their performance is evaluated regarding accuracy and sensitivity. The resultsaffirm the potential of the proposed approach for automated cell nucleiidentification. These AI advancements in digital pathology open avenuesfor early and precise cancer detection, influencing clinical practices andpatient well-being and improving diagnostic efficiency.
PB Springer
YR 2024
FD 2024
LK https://hdl.handle.net/10630/32260
UL https://hdl.handle.net/10630/32260
LA eng
NO Política de acceso abierto tomada de: https://www.springernature.com/gp/open-research/policies/book-policies
NO Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 12 abr 2026