Frontiers

Interpretable vision mamba for SAR images classification

Deep learning techniques have been successfully applied to object classification in Synthetic Aperture Radar (SAR) images, achieving remarkable performance. However, the current Transformer ...
Frontiers

Traditional machine learning in biomedical image analysis: before you go too deep

Traditional machine learning (TML) algorithms remain indispensable tools for the analysis of biomedical images, offering significant advantages in multimodal data integration, interpretability, ...
Oak Ridge National Lab

DeepHyper: A Python Package for Massively Parallel Hyperparameter Optimization in Machine Learning...

Machine learning models are increasingly applied across scientific disciplines, yet their effectiveness often hinges on heuristic decisions such as data transformations, training strategies, and model ...
GitHub

Azure AutoML for Images - Image Classification

This repository contains Python notebooks demonstrating image classification using Azure AutoML for Images. These notebooks provide practical examples of building computer vision models for various ...
Nature

An automated hybrid deep learning framework for paddy leaf disease identification and classification

Nowadays, agriculture serves as a cornerstone for addressing the nutritional needs of an expanding population. Agriculture, fisheries, and forestry sectors contribute approximately 18% to the GDP.
Nature

Deep learning classification integrating embryo images with associated clinical information from ART cycles

An advanced Artificial Intelligence (AI) model that leverages cutting-edge computer vision techniques to analyse embryo images and clinical data, enabling accurate prediction of clinical pregnancy ...
techxplore

AI model classifies images with a hierarchical tree from broad to specific

A new AI model, H-CAST, groups fine details into object-level concepts as attention moves from lower to high layers, outputting a classification tree—such as bird, eagle, bald eagle—rather than ...
LinkedIn

Deep Learning: How Machines Learn to Think in Layers

Imagine a toddler learning to identify animals. At first, she might confuse a cat with a fox. But over time—after seeing hundreds of examples—she begins to recognize the subtle differences. Deep ...
IEEE

Improving Image Classification Using Attention Mechanisms in Deep Learning Models

Abstract: In this study, we explore the application of attention mechanisms to enhance deep learning models in the context of image classification. We assess several types of attention mechanisms ...
LinkedIn

XAI and Deep Learning in Brain Tumors Classification

In recent years, the combination of Explainable Artificial Intelligence (XAI) with deep learning techniques has significantly transformed the area of medical imaging, particularly in the ...
IEEE

Deep Active Learning for Image Hierarchical Classification by Introducing Dependencies and Constraints Between Classes

Abstract: Deep active learning (DeepAL) extends supervised deep learning to human-machine interactive scenarios with limited annotation budgets. Most existing DeepAL approaches for visual recognition ...