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Listar por autor "Silvestre Molina, Rafael"

Mostrando ítems 1-7 de 7

A New Optical Density Granulometry-Based Descriptor for the Classification of Prostate Histological Images Using Shallow and Deep Gaussian Processes

Esteban, A. E.; López-Pérez, Miguel; Colomer, Adrián; Sales, Maria A.; Molina, Rafael; Naranjo Ornedo, Valeriana (Elsevier, 2019-09)

[EN] Background and objective Prostate cancer is one of the most common male tumors. The increasing use of whole slide digital scanners has led to an enormous interest in the application of machine learning techniques to ...
A TV-based image processing framework for blind color deconvolution and classification of histological images

Pérez-Bueno, Fernando; López-Pérez, Miguel; Vega, Miguel; Mateos, Javier; Naranjo Ornedo, Valeriana; Molina, Rafael; Katsaggelos, Aggelos K. (Elsevier, 2020-06)

[EN] In digital histopathological image analysis, two conflicting objectives are often pursued: closeness to the original tissue and high classification performance. The former objective tries to recover images (stains) ...
Advancing Content-Based Histopathological Image Retrieval Pre-Processing: A Comparative Analysis of the Effects of Color Normalization Techniques

Tabatabaei, Zahra; Pérez Bueno, Fernando; Colomer, Adrián; Oliver Moll, Javier; Molina, Rafael; Naranjo Ornedo, Valeriana (MDPI AG, 2024-03)

[EN] Content-Based Histopathological Image Retrieval (CBHIR) is a search technique based on the visual content and histopathological features of whole-slide images (WSIs). CBHIR tools assist pathologists to obtain a faster ...
Campaña de sensibilización ambiental para los liceos del departamento de Colonia, Uruguay

Silvestre Molina, Rafael (Universitat Politècnica de València, 2016-04-26)

El objetivo de este Trabajo Final de Carrera, es el de diseñar una campaña de sensibilización ambiental dirigida a los jóvenes estudiantes de los liceos (1) del Departamento de Colonia, Uruguay. Con la intención de poner ...
Efficient Cancer Classification by Coupling Semi Supervised and Multiple Instance Learning

Schmidt, Arne; Silva-Rodríguez, Julio; Molina, Rafael; Naranjo Ornedo, Valeriana (Institute of Electrical and Electronics Engineers, 2022)

[EN] The annotation of large datasets is often the bottleneck in the successful application of artificial intelligence in computational pathology. For this reason recently Multiple Instance Learning (MIL) and Semi Supervised ...
Going deeper through the Gleason scoring scale: An automatic end-to-end system for histology prostate grading and cribriform pattern detection

Silva-Rodríguez, Julio; Colomer, Adrián; Sales, María A.; Molina, Rafael; Naranjo Ornedo, Valeriana (Elsevier, 2020-10)

[EN] Background and Objective: Prostate cancer is one of the most common diseases affecting men worldwide. The Gleason scoring system is the primary diagnostic and prognostic tool for prostate cancer. Further-more, recent ...
Proportion constrained weakly supervised histopathology image classification

Silva-Rodríguez, Julio; Schmidt, Arne; Sales, María A.; Molina, Rafael; Naranjo Ornedo, Valeriana (Elsevier, 2022-08)

[EN] Multiple instance learning (MIL) deals with data grouped into bags of instances, of which only the global information is known. In recent years, this weakly supervised learning paradigm has become very popular in ...