The CrowdGleason dataset: Learning the Gleason grade from crowds and experts.

Background: Currently, prostate cancer (PCa) diagnosis relies on the human analysis of prostate biopsy Whole Slide Images (WSIs) using the Gleason score. Since this process is error-prone and time-consuming, recent advances in machine learning have promoted the use of automated systems to assist pathologists. Unfortunately, labeled datasets for training and validation are scarce due to the need for expert pathologists to provide ground-truth labels.

Plasma membrane repair defect in Alzheimer's disease neurons is driven by the reduced dysferlin expression.

Alzheimer's disease (AD) is the most common neurodegenerative disease, and a defect in neuronal plasma membrane repair could exacerbate neurotoxicity, neuronal death, and disease progression. In this study, application of AD patient cerebrospinal fluid (CSF) and recombinant human Aβ to otherwise healthy neurons induces defective neuronal plasma membrane repair in vitro and ex vivo. We identified Aβ as the biochemical component in patient CSF leading to compromised repair capacity and depleting Aβ rescued repair capacity.

Are you sure it's an artifact? Artifact detection and uncertainty quantification in histological images.

Modern cancer diagnostics involves extracting tissue specimens from suspicious areas and conducting histotechnical procedures to prepare a digitized glass slide, called Whole Slide Image (WSI), for further examination. These procedures frequently introduce different types of artifacts in the obtained WSI, and histological artifacts might influence Computational Pathology (CPATH) systems further down to a diagnostic pipeline if not excluded or handled. Deep Convolutional Neural Networks (DCNNs) have achieved promising results for the detection of some WSI artifacts, however, they do not incorporate uncertainty in their predictions.

Learning from crowds for automated histopathological image segmentation.

Automated semantic segmentation of histopathological images is an essential task in Computational Pathology (CPATH). The main limitation of Deep Learning (DL) to address this task is the scarcity of expert annotations. Crowdsourcing (CR) has emerged as a promising solution to reduce the individual (expert) annotation cost by distributing the labeling effort among a group of (non-expert) annotators.

Annotation protocol and crowdsourcing multiple instance learning classification of skin histological images: The CR-AI4SkIN dataset.

Digital Pathology (DP) has experienced a significant growth in recent years and has become an essential tool for diagnosing and prognosis of tumors. The availability of Whole Slide Images (WSIs) and the implementation of Deep Learning (DL) algorithms have paved the way for the appearance of Artificial Intelligence (AI) systems that support the diagnosis process. These systems require extensive and varied data for their training to be successful.

Deep Gaussian processes for multiple instance learning: Application to CT intracranial hemorrhage detection.

Background And Objective: Intracranial hemorrhage (ICH) is a life-threatening emergency that can lead to brain damage or death, with high rates of mortality and morbidity. The fast and accurate detection of ICH is important for the patient to get an early and efficient treatment. To improve this diagnostic process, the application of Deep Learning (DL) models on head CT scans is an active area of research.

A new optical density granulometry-based descriptor for the classification of prostate histological images using shallow and deep Gaussian processes.

Background And Objective: Prostate cancer is one of the most common male tumors. The increasing use of whole slide digital scanners has led to an enormous interest in the application of machine learning techniques to histopathological image classification. Here we introduce a novel family of morphological descriptors which, extracted in the appropriate image space and combined with shallow and deep Gaussian process based classifiers, improves early prostate cancer diagnosis.

[Simón de Tovar (1528-1596): family networks, American nature and trade in curiosities in 16th century Seville].

The central character of this work is Simón de Tovar, a citizen of late 16th century Seville, who was an outstanding collector of American plants. Beyond the traditional historiography of early modern Spanish botany, Tovar's activities have been set in the context of an expanding trade in marvels and curiosities.

[Some features of the relationship between Lullism and pseudo-Lullism in the early modern times].

In this work, the author presents, following a chronologic sequence, some aspects of Llull's image that became controversial during the 16th century, especially the discussion about the idea of Llull as alchemist, which developed around the 16th century. To this end, the author analyses the origin and development of the polemic since the 14th century, including the "official" positions. At the same time, the two positions (Llull the achemist and Llull the philosopher) are related to similar occurrences outside the Iberian Peninsular, especially in France and Italy.