170 citations · 382 across the 18 of their papers we have counts for
18 papers
Class Adaptive Conformal Training
Badr-Eddine Marani, Julio Silva-Rodriguez, Ismail Ben Ayed +3
Deep neural networks have achieved remarkable success across a variety of tasks, yet they often suffer from unreliable probability estimates. As a result, they can be overconfident…
Trustworthy Few-Shot Transfer of Medical VLMs through Split Conformal Prediction
Julio Silva-Rodríguez, Ismail Ben Ayed, Jose Dolz
Medical vision-language models (VLMs) have demonstrated unprecedented transfer capabilities and are being increasingly adopted for data-efficient image classification. Despite its…
Few-Shot, Now for Real: Medical VLMs Adaptation without Balanced Sets or Validation
Julio Silva-Rodríguez, Fereshteh Shakeri, Houda Bahig +2
Vision-language models (VLMs) are gaining attention in medical image analysis. These are pre-trained on large, heterogeneous data sources, yielding rich and transferable representa…
Semantic Anchor Transport: Robust Test-Time Adaptation for Vision-Language Models
Shambhavi Mishra, Julio Silva-Rodriguez, Ismail Ben Ayed +2
Large pre-trained vision-language models (VLMs), such as CLIP, have shown unprecedented zero-shot performance across a wide range of tasks. Nevertheless, these models may be unreli…
Robust Calibration of Large Vision-Language Adapters
Balamurali Murugesan, Julio Silva-Rodriguez, Ismail Ben Ayed +1
This paper addresses the critical issue of miscalibration in CLIP-based model adaptation, particularly in the challenging scenario of out-of-distribution (OOD) samples, which has b…
Class and Region-Adaptive Constraints for Network Calibration
Balamurali Murugesan, Julio Silva-Rodriguez, Ismail Ben Ayed +1
In this work, we present a novel approach to calibrate segmentation networks that considers the inherent challenges posed by different categories and object regions. In particular,…
Exploring the Transferability of a Foundation Model for Fundus Images: Application to Hypertensive Retinopathy
Julio Silva-Rodriguez, Jihed Chelbi, Waziha Kabir +4
Using deep learning models pre-trained on Imagenet is the traditional solution for medical image classification to deal with data scarcity. Nevertheless, relevant literature suppor…
Attention to detail: inter-resolution knowledge distillation
Rocío del Amor, Julio Silva-Rodríguez, Adrián Colomer +1
The development of computer vision solutions for gigapixel images in digital pathology is hampered by significant computational limitations due to the large size of whole slide ima…
A Closer Look at the Few-Shot Adaptation of Large Vision-Language Models
Julio Silva-Rodríguez, Sina Hajimiri, Ismail Ben Ayed +1
Efficient transfer learning (ETL) is receiving increasing attention to adapt large pre-trained language-vision models on downstream tasks with a few labeled samples. While signific…
A Foundation Language-Image Model of the Retina (FLAIR): Encoding Expert Knowledge in Text Supervision
Julio Silva-Rodríguez, Hadi Chakor, Riadh Kobbi +2
Foundation vision-language models are currently transforming computer vision, and are on the rise in medical imaging fueled by their very promising generalization capabilities. How…
HistoColAi: An Open-Source Web Platform for Collaborative Digital Histology Image Annotation with AI-Driven Predictive Integration
Cristian Camilo Pulgarín-Ospina, Rocío del Amor, Adrián Colomera +2
Digital pathology has become a standard in the pathology workflow due to its many benefits. These include the level of detail of the whole slide images generated and the potential…
Challenging mitosis detection algorithms: Global labels allow centroid localization
Claudio Fernandez-Martín, Umay Kiraz, Julio Silva-Rodríguez +3
Mitotic activity is a crucial proliferation biomarker for the diagnosis and prognosis of different types of cancers. Nevertheless, mitosis counting is a cumbersome process for path…
Constrained unsupervised anomaly segmentation
Julio Silva-Rodríguez, Valery Naranjo, Jose Dolz
Current unsupervised anomaly localization approaches rely on generative models to learn the distribution of normal images, which is later used to identify potential anomalous regio…
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…
Gleason Grading of Histology Prostate Images through Semantic Segmentation via Residual U-Net
Amartya Kalapahar, Julio Silva-Rodríguez, Adrián Colomer +2
Worldwide, prostate cancer is one of the main cancers affecting men. The final diagnosis of prostate cancer is based on the visual detection of Gleason patterns in prostate biopsy…