This is a package with state of the art methods for Explainable AI for computer vision. This can be used for diagnosing model predictions, either in production or while developing models. The aim is ...

This project focuses on classifying breast cancer histopathology images into two classes: benign and malignant. The main problem is that breast cancer diagnosis from histopathology images requires ...

Researchers have developed AdapGNN, a novel model-agnostic framework that addresses the oversmoothing problem in graph neural ...

Artificial intelligence (AI) is increasingly reshaping diagnostic pathology, with breast pathology representing one of the most advanced and clinically impactful areas of adoption. Despite rapid ...

Abstract: As Artificial Intelligence (AI) is increasingly utilized in dermatology, ensuring fairness in the development of Machine Learning models is crucial, particularly in skin lesion ...

The RBI has proposed a comprehensive regulatory framework to govern the use of artificial intelligence and other ...

Abstract: In this paper, we present HybiNet, a novel hybrid deep learning model, for multi-label classification on chest Xrays, that takes advantage of EfficientNetB0, DenseNet121, and Xception with ...

The rise of autonomous AI agents in enterprise IT is fundamentally challenging the established business and revenue models of ...

The central bank's draft guidelines require board-approved model risk frameworks, stronger oversight of AI models and ...

You learn about Classification. Most of my learning was about regression, where models predict numbers. Now I know many problems involve predicting categories. Some examples are: - Detecting spam ...

Artificial Intelligence-Assisted Medical Imaging Solutions for Integrating Pathology and Radiology Automated Systems - Volume III ...

2. Comments on the draft Guidanceare invited from regulated entities, members of public and other stakeholders by July 24, 2026. The comments / feedback may be submitted through the link under the ...