7 papers
Initialization matters in few-shot adaptation of vision-language models for histopathological image classification
Pablo Meseguer, RocÃo del Amor, Valery Naranjo
Vision language models (VLM) pre-trained on datasets of histopathological image-caption pairs enabled zero-shot slide-level classification. The ability of VLM image encoders to ext…
Zero-shot segmentation of skin tumors in whole-slide images with vision-language foundation models
Santiago Moreno, Pablo Meseguer, RocÃo del Amor +1
Accurate annotation of cutaneous neoplasm biopsies represents a major challenge due to their wide morphological variability, overlapping histological patterns, and the subtle disti…
Benchmarking histopathology foundation models in a multi-center dataset for skin cancer subtyping
Pablo Meseguer, RocÃo del Amor, Valery Naranjo
Pretraining on large-scale, in-domain datasets grants histopathology foundation models (FM) the ability to learn task-agnostic data representations, enhancing transfer learning on…
Exploring visual language models as a powerful tool in the diagnosis of Ewing Sarcoma
Alvaro Pastor-Naranjo, Pablo Meseguer, RocÃo del Amor +6
Ewing's sarcoma (ES), characterized by a high density of small round blue cells without structural organization, presents a significant health concern, particularly among adolescen…
Enhancing Whole Slide Image Classification through Supervised Contrastive Domain Adaptation
Ilán Carretero, Pablo Meseguer, RocÃo del Amor +1
Domain shift in the field of histopathological imaging is a common phenomenon due to the intra- and inter-hospital variability of staining and digitization protocols. The implement…
Foundation Models for Slide-level Cancer Subtyping in Digital Pathology
Pablo Meseguer, RocÃo del Amor, Adrian Colomer +1
Since the emergence of the ImageNet dataset, the pretraining and fine-tuning approach has become widely adopted in computer vision due to the ability of ImageNet-pretrained models…