Area of research
Artificial Intelligence · Radiology, Nuclear Medicine and Imaging
Research interest
Research interests include AI in cancer detection, Retinal Imaging and Analysis, Digital Imaging for Blood Diseases, and Glaucoma and retinal disorders.
Fronto-caudate and callosal microstructural alterations: unveiling multimodal MRI biomarkers in early Parkinson's disease.
The puzzling Spitz tumours: is artificial intelligence the key to their understanding?
Artificial Intelligence Enabled Histological Prediction of Remission or Activity and Clinical Outcomes in Ulcerative Colitis
WWFedCBMIR: World-Wide Federated Content-Based Medical Image Retrieval.
PICaSSO Histologic Remission Index (PHRI) in ulcerative colitis: development of a novel simplified histological score for monitoring mucosal healing and predicting clinical outcomes and its applicability in an artificial intelligence system
PICaSSO Histologic Remission Index (PHRI) in ulcerative colitis: development of a novel simplified histological score for monitoring mucosal healing and predicting clinical outcomes and its applicability in an artificial intelligence system.
A virtual chromoendoscopy artificial intelligence system to detect endoscopic and histologic activity/remission and predict clinical outcomes in ulcerative colitis
Constrained multiple instance learning for ulcerative colitis prediction using histological images
Constrained unsupervised anomaly segmentation.
Automatic characterization of human embryos at day 4 post-insemination from time-lapse imaging using supervised contrastive learning and inductive transfer learning techniques.
Proportion constrained weakly supervised histopathology image classification.
WeGleNet: A weakly-supervised convolutional neural network for the semantic segmentation of Gleason grades in prostate histology images.
An attention-based weakly supervised framework for spitzoid melanocytic lesion diagnosis in whole slide images.
Glaucoma Detection from Raw SD-OCT Volumes: A Novel Approach Focused on Spatial Dependencies.
Self-Learning for Weakly Supervised Gleason Grading of Local Patterns.
Circumpapillary OCT-focused hybrid learning for glaucoma grading using tailored prototypical neural networks.
A novel self-learning framework for bladder cancer grading using histopathological images.
Contribution of Gray Matter Atrophy and White Matter Damage to Cognitive Impairment in Mildly Disabled Relapsing-Remitting Multiple Sclerosis Patients.
Retinal layer segmentation in rodent OCT images: Local intensity profiles & fully convolutional neural networks.
Automatic Segmentation of the Retinal Nerve Fiber Layer by Means of Mathematical Morphology and Deformable Models in 2D Optical Coherence Tomography Imaging.
Supervised Domain Adaptation for Automated Semantic Segmentation of the Atrial Cavity.
REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs.
Going deeper through the Gleason scoring scale: An automatic end-to-end system for histology prostate grading and cribriform pattern detection.
Novel and conventional embryo parameters as input data for artificial neural networks: an artificial intelligence model applied for prediction of the implantation potential.
Automatic Segmentation of Epidermis and Hair Follicles in Optical Coherence Tomography Images of Normal Skin by Convolutional Neural Networks.
Automatic evaluation of degree of cleanliness in capsule endoscopy based on a novel CNN architecture.
Detection of Early Signs of Diabetic Retinopathy Based on Textural and Morphological Information in Fundus Images.
The Influence of Each Facial Feature on How We Perceive and Interpret Human Faces.
Automatic identification of stimulation activities during newborn resuscitation using ECG and accelerometer signals.
REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs