IEEE

Evaluating Self-Supervised Learning Models for Multi-Label Pathology Detection: A Benchmark Against Supervised Learning

Abstract: The analysis and objective of the current work is to check the performance of SSL Models in multi-label classification on the available dataset of NIH Chest X-ray images which is a ...
IEEE

Multilingual and Multi-Class Sentiment Classification Using Machine Learning, BERT, and GPT-4o-mini

Abstract: In this study, we investigate multilingual and multiclass sentiment classification by analyzing datasets in Turkish, English, and Italian. The proposed approach consists of three main stages ...
eLife

Retrosplenial cortex enables context-dependent goal-directed sensorimotor transformation

This important study combines optogenetic manipulations and wide-field imaging to show that the retrosplenial cortex controls behavioral responses to whisker deflection in a context-dependent manner.
Microsoft

Multi-Label Classification for Colon Cancer Using Histopathological Images

Colon cancer classification has a significant guidance value in clinical diagnoses and medical prognoses. The classification of colon cancers with high accuracy is the premise of efficient treatment.
Journal of Medical Internet Research

Using Machine Learning Methods to Predict Early Treatment Outcomes for Multidrug-Resistant or Rifampicin-Resistant Tuberculosis to Enhance Patient Cure Rates: Development and ...

However, the absence of validated predictive models remains a significant challenge. Objective: This study compared a conventional logistic regression model with machine learning (ML) models using ...