Breast Cancer Histopathological Image Classification Based on Graph Assisted Global Reasoning.

Breast cancer ranks as the most prevalent cancer among women globally. Histopathological image analysis stands as one of the most reliable methods for tumor detection. This study aims to utilize deep learning to extract histopathological features and automatically identify tumor information, thereby assisting doctors in high-precision pathological diagnosis.

Clinical Insights on My Acute Ischemic Stroke Caused by a Carotid Web.

Acute ischemic stroke, a medical emergency caused by reduced cerebral blood flow, results in brain cell damage. While commonly associated with older individuals, strokes can also occur in young and middle-aged adults, posing significant socio-economic and health challenges due to the long-term impact of the condition. This poses significant socio-economic and health challenges because stroke is a leading cause of disability and mortality.

Enhance health evidence quality in classification tasks: A triangulation approach utilizing case-based reasoning and process features.

Objective: Machine learning (ML) has enabled healthcare discoveries by facilitating efficient modeling, such as for cancer screening. Unlike clinical trials, real-world data used in ML are often gathered for multiple purposes, leading to bias and missing information for a specific classification task. This challenge is especially pronounced in healthcare because of stringent ethical considerations and resource constraints.

Investigating the associations between personality functioning, cognitive biases, and (non-)perceptive clinical high-risk symptoms of psychosis in the community.

Background: Beyond psychosis prediction, clinical high-risk (CHR-P) symptoms show clinical relevance by their association with functional impairments and psychopathology, including personality pathology. Impaired personality functioning is prioritized in recent dimensional personality disorder models (DSM-5, ICD-11), yet underexplored in CHR-P, as are associations with cognitive biases, which early studies indicate as possibly linking CHR-P-symptoms and personality pathology.

Methods: A community sample ( 444, 17-60 years, 61.