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.

Effects of reduced crude protein diets while maintaining essential amino acid concentrations on growth performance, nitrogen output, ammonia production, and meat yield.

An experiment was conducted to determine the effect of feeding reduced crude protein (CP) diets to Ross × Ross 708 male broilers while providing adequate essential amino acid (AA) concentrations on growth performance, nitrogen (N) and ammonia output, and carcass characteristics from d 1 to 33 post hatch. Birds received 1 of 6 dietary treatments (10 replicate pens per treatment) varying in CP content. Diet 1 (control) was formulated with DL-Met, L-Lys, and L-Thr (23.

Uninformed Teacher-Student for hard-samples distillation in weakly supervised mitosis localization.

Background And Objective: Mitotic activity is a crucial biomarker for diagnosing and predicting outcomes for different types of cancers, particularly breast cancer. However, manual mitosis counting is challenging and time-consuming for pathologists, with moderate reproducibility due to biopsy slide size, low mitotic cell density, and pattern heterogeneity. In recent years, deep learning methods based on convolutional neural networks (CNNs) have been proposed to address these limitations.

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.

WWFedCBMIR: World-Wide Federated Content-Based Medical Image Retrieval.

The paper proposes a federated content-based medical image retrieval (FedCBMIR) tool that utilizes federated learning (FL) to address the challenges of acquiring a diverse medical data set for training CBMIR models. CBMIR is a tool to find the most similar cases in the data set to assist pathologists. Training such a tool necessitates a pool of whole-slide images (WSIs) to train the feature extractor (FE) to extract an optimal embedding vector.

A Spitzoid Tumor dataset with clinical metadata and Whole Slide Images for Deep Learning models.

Spitzoid tumors (ST) are a group of melanocytic tumors of high diagnostic complexity. Since 1948, when Sophie Spitz first described them, the diagnostic uncertainty remains until now, especially in the intermediate category known as Spitz tumor of unknown malignant potential (STUMP) or atypical Spitz tumor. Studies developing deep learning (DL) models to diagnose melanocytic tumors using whole slide imaging (WSI) are scarce, and few used ST for analysis, excluding STUMP.

Durable 3D murine ex vivo retina glaucoma models for optical coherence tomography.

Durable and standardized phantoms with optical properties similar to native healthy and disease-like biological tissues are essential tools for the development, performance testing, calibration and comparison of label-free high-resolution optical coherence tomography (HR-OCT) systems. Available phantoms are based on artificial materials and reflect thus only partially ocular properties. To address this limitation, we have performed investigations on the establishment of durable tissue phantoms from ex vivo mouse retina for enhanced reproduction of in vivo structure and complexity.

Deep learning system for classification of ploidy status using time-lapse videos.

Objective: To develop a spatiotemporal model for de prediction of euploid and aneuploid embryos using time-lapse videos from 10-115 hours after insemination (hpi).

Design: Retrospective study.

Main Outcome Measures: The research used an end-to-end approach to develop an automated artificial intelligence system capable of extracting features from images and classifying them, considering spatiotemporal dependencies.

ProGleason-GAN: Conditional progressive growing GAN for prostatic cancer Gleason grade patch synthesis.

Background And Objective: Prostate cancer is one of the most common diseases affecting men. The main diagnostic and prognostic reference tool is the Gleason scoring system. An expert pathologist assigns a Gleason grade to a sample of prostate tissue.

Labeling confidence for uncertainty-aware histology image classification.

Deep learning-based models applied to digital pathology require large, curated datasets with high-quality (HQ) annotations to perform correctly. In many cases, recruiting expert pathologists to annotate large databases is not feasible, and it is necessary to collect additional labeled data with varying label qualities, e.g.

Artificial Intelligence Enabled Histological Prediction of Remission or Activity and Clinical Outcomes in Ulcerative Colitis.

Background & Aims: Microscopic inflammation has significant prognostic value in ulcerative colitis (UC); however, its assessment is complex with high interobserver variability. We aimed to develop and validate an artificial intelligence (AI) computer-aided diagnosis system to evaluate UC biopsies and predict prognosis.

Methods: A total of 535 digitalized biopsies (273 patients) were graded according to the PICaSSO Histologic Remission Index (PHRI), Robarts, and Nancy Histological Index.

Deep Learning for Skin Melanocytic Tumors in Whole-Slide Images: A Systematic Review.

The rise of Artificial Intelligence (AI) has shown promising performance as a support tool in clinical pathology workflows. In addition to the well-known interobserver variability between dermatopathologists, melanomas present a significant challenge in their histological interpretation. This study aims to analyze all previously published studies on whole-slide images of melanocytic tumors that rely on deep learning techniques for automatic image analysis.

Effect of a high-fat diet and leptin on STAT3 phosphorylation in hippocampal astrocytes.

Objective: The aim of the current study was to evaluate the influence of HFD on the functionality of LepR by quantifying phosphorylated levels of 705Tyr-STAT3 in hippocampus astrocytes from mice that consumed an HFD either during the juvenile or the adult period.

Methods: Five- and eight-week-old male mice, fed during 8 weeks with either control chow or HFD, received a single dose of leptin and their brains were prepared for immunofluorescence to identify double-positive GFAP/p705Tyr-STAT3 cells.

Results: HFD intake led to increased pSTAT3 immunoreactivity in GFAP+ cells in the CA1/CA3 hippocampus areas.

Influence of Expander Conditioning Prior to Pelleting on Pellet Quality, Broiler Digestibility and Performance at Constant Amino Acids Composition while Decreasing AME.

Physical pellet quality and AMEN concentration are strongly related to each other in broiler feeding. A study was conducted to evaluate the relationship between dietary AMEN concentration and feed processing on pellet quality, nutrient digestibility, broiler performance, serum markers, and yield of commercial cuts. Six diets were formulated.

Towards Large Particle Size in Compound Feed: Using Expander Conditioning Prior to Pelleting Improves Pellet Quality and Growth Performance of Broilers.

During the processing of compound feed for broilers, several changes occur that affect the physical and probably the nutritional properties of pellets, influencing animal performance. The effects of mill type, particle size (PS) and expander conditioning prior to pelleting (E + P) were combined to generate pellets. A 2 × 3 × 2 factorial arrangement was designed with two mill types (a hammer mill (HM) or roller mill (RM)), three PSs (0.

A virtual chromoendoscopy artificial intelligence system to detect endoscopic and histologic activity/remission and predict clinical outcomes in ulcerative colitis.

Background: Endoscopic and histological remission (ER, HR) are therapeutic targets in ulcerative colitis (UC). Virtual chromoendoscopy (VCE) improves endoscopic assessment and the prediction of histology; however, interobserver variability limits standardized endoscopic assessment. We aimed to develop an artificial intelligence (AI) tool to distinguish ER/activity, and predict histology and risk of flare from white-light endoscopy (WLE) and VCE videos.

Constrained multiple instance learning for ulcerative colitis prediction using histological images.

Background And Objective: Ulcerative colitis (UC) is an inflammatory bowel disease (IBD) affecting the colon and the rectum characterized by a remitting-relapsing course. To detect mucosal inflammation associated with UC, histology is considered the most stringent criteria. In turn, histologic remission (HR) correlates with improved clinical outcomes and has been recently recognized as a desirable treatment target.

Constrained unsupervised anomaly segmentation.

Current unsupervised anomaly localization approaches rely on generative models to learn the distribution of normal images, which is later used to identify potential anomalous regions derived from errors on the reconstructed images. To address the limitations of residual-based anomaly localization, very recent literature has focused on attention maps, by integrating supervision on them in the form of homogenization constraints. In this work, we propose a novel formulation that addresses the problem in a more principled manner, leveraging well-known knowledge in constrained optimization.

Proportion constrained weakly supervised histopathology image classification.

Multiple instance learning (MIL) deals with data grouped into bags of instances, of which only the global information is known. In recent years, this weakly supervised learning paradigm has become very popular in histological image analysis because it alleviates the burden of labeling all cancerous regions of large Whole Slide Images (WSIs) in detail. However, these methods require large datasets to perform properly, and many approaches only focus on simple binary classification.

Automatic characterization of human embryos at day 4 post-insemination from time-lapse imaging using supervised contrastive learning and inductive transfer learning techniques.

Background: Embryo morphology is a predictive marker for implantation success and ultimately live births. Viability evaluation and quality grading are commonly used to select the embryo with the highest implantation potential. However, the traditional method of manual embryo assessment is time-consuming and highly susceptible to inter- and intra-observer variability.