170 citations · 310 across the 19 of their papers we have counts for
12 papers · 1 filter
Towards More Transparent and Accurate Cancer Diagnosis with an Unsupervised CAE Approach
Zahra Tabatabaei, Adrian Colomer, Javier Oliver Moll +1
Digital pathology has revolutionized cancer diagnosis by leveraging Content-Based Medical Image Retrieval (CBMIR) for analyzing histopathological Whole Slide Images (WSIs). CBMIR e…
WWFedCBMIR: World-Wide Federated Content-Based Medical Image Retrieval
Zahra Tabatabaei, Yuandou Wang, Adrián Colomer +3
The paper proposes a Federated Content-Based Medical Image Retrieval (FedCBMIR) platform that utilizes Federated Learning (FL) to address the challenges of acquiring a diverse medi…
Self-supervised learning of a tailored Convolutional Auto Encoder for histopathological prostate grading
Zahra Tabatabaei, Adrian colomer, Kjersti Engan +2
According to GLOBOCAN 2020, prostate cancer is the second most common cancer in men worldwide and the fourth most prevalent cancer overall. For pathologists, grading prostate cance…
A Novel Self-Learning Framework for Bladder Cancer Grading Using Histopathological Images
Gabriel García, Anna Esteve, Adrián Colomer +2
Recently, bladder cancer has been significantly increased in terms of incidence and mortality. Currently, two subtypes are known based on tumour growth: non-muscle invasive (NMIBC)…
Circumpapillary OCT-Focused Hybrid Learning for Glaucoma Grading Using Tailored Prototypical Neural Networks
Gabriel García, Rocío del Amor, Adrián Colomer +3
Glaucoma is one of the leading causes of blindness worldwide and Optical Coherence Tomography (OCT) is the quintessential imaging technique for its detection. Unlike most of the st…
Prostate Gland Segmentation in Histology Images via Residual and Multi-Resolution U-Net
Julio Silva-Rodríguez, Elena Payá-Bosch, Gabriel García +2
Prostate cancer is one of the most prevalent cancers worldwide. One of the key factors in reducing its mortality is based on early detection. The computer-aided diagnosis systems f…
Going Deeper through the Gleason Scoring Scale: An Automatic end-to-end System for Histology Prostate Grading and Cribriform Pattern Detection
Julio Silva-Rodríguez, Adrián Colomer, María A. Sales +2
The Gleason scoring system is the primary diagnostic and prognostic tool for prostate cancer. In recent years, with the development of digitisation devices, the use of computer vis…
WeGleNet: A Weakly-Supervised Convolutional Neural Network for the Semantic Segmentation of Gleason Grades in Prostate Histology Images
Julio Silva-Rodríguez, Adrián Colomer, Valery Naranjo
Prostate cancer is one of the main diseases affecting men worldwide. The Gleason scoring system is the primary diagnostic tool for prostate cancer. This is obtained via the visual…
Self-learning for weakly supervised Gleason grading of local patterns
Julio Silva-Rodríguez, Adrián Colomer, Jose Dolz +1
Prostate cancer is one of the main diseases affecting men worldwide. The gold standard for diagnosis and prognosis is the Gleason grading system. In this process, pathologists manu…
An Attention-based Weakly Supervised framework for Spitzoid Melanocytic Lesion Diagnosis in WSI
Rocío del Amor, Laëtitia Launet, Adrián Colomer +4
Melanoma is an aggressive neoplasm responsible for the majority of deaths from skin cancer. Specifically, spitzoid melanocytic tumors are one of the most challenging melanocytic le…
Analysis of Hand-Crafted and Automatic-Learned Features for Glaucoma Detection Through Raw Circmpapillary OCT Images
Gabriel García, Adrián Colomer, Valery Naranjo
Taking into account that glaucoma is the leading cause of blindness worldwide, we propose in this paper three different learning methodologies for glaucoma detection in order to el…
Glaucoma Detection From Raw Circumapillary OCT Images Using Fully Convolutional Neural Networks
Gabriel García, Rocío del Amor, Adrián Colomer +1
Nowadays, glaucoma is the leading cause of blindness worldwide. We propose in this paper two different deep-learning-based approaches to address glaucoma detection just from raw ci…