HistoColAi: An open-source web platform for collaborative digital histology image annotation with AI-driven predictive integration.

Digital pathology is now a standard component of the pathology workflow, offering numerous benefits such as high-detail whole slide images and the capability for immediate case sharing between hospitals. Recent advances in deep learning-based methods for image analysis make them a potential aid in digital pathology. However, A significant challenge in developing computer-aided diagnostic systems for pathology is the lack of intuitive, open-source web applications for data annotation.

Beyond nest size: the clinicopathological spectrum of large nested melanocytic tumours and the value of comparative genomic hybridisation and messenger RNA expression analysis.

Large nested melanomas (LNMs) are a rare subtype of naevoid melanoma consisting of large junctional melanocytic nests that are more common in older individuals and/or associated with sun damage. However, the presence of large melanocytic nests alone does not lead to a diagnosis of malignancy, ​as they can also be found in melanocytic naevi. LNMs are challenging because they lack most classic histological features of malignancy and require thorough clinicopathological evaluation.

EEG-based responses of patients with disorders of consciousness and healthy controls to familiar and non-familiar emotional videos.

Objective: To investigate the differences in the brain responses of healthy controls (HC) and patients with disorders of consciousness (DOC) to familiar and non-familiar audiovisual stimuli and their consistency with the clinical progress.

Methods: EEG responses of 19 HC and 19 patients with DOC were recorded while watching emotionally-valenced familiar and non-familiar videos. Differential entropy of the EEG recordings was used to train machine learning models aimed to distinguish brain responses to stimuli type.

MICIL: Multiple-Instance Class-Incremental Learning for skin cancer whole slide images.

Artificial intelligence (AI) agents encounter the problem of catastrophic forgetting when they are trained in sequentially with new data batches. This issue poses a barrier to the implementation of AI-based models in tasks that involve ongoing evolution, such as cancer prediction. Moreover, whole slide images (WSI) play a crucial role in cancer management, and their automated analysis has become increasingly popular in assisting pathologists during the diagnosis process.