AIMC Topic: Deep Learning

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RadField3D: a data generator and data format for deep learning in radiation-protection dosimetry for medical applications.

Journal of radiological protection : official journal of the Society for Radiological Protection
In this research work, we present our open-source Geant4-based Monte-Carlo simulation application, called RadField3D, for generating three-dimensional radiation field datasets for dosimetry. Accompanying, we introduce a fast, machine-interpretable da...

Deep Learning-Based Classification of CRISPR Loci Using Repeat Sequences.

ACS synthetic biology
With the widespread application of the CRISPR-Cas system in gene editing and related fields, along with the increasing availability of metagenomic data, the demand for detecting and classifying CRISPR-Cas systems in metagenomic data sets has grown si...

Next-Level Prediction of Structural Progression in Knee Osteoarthritis: A Perspective.

International journal of molecular sciences
Osteoarthritis (OA) is a prevalent and disabling chronic disease, with knee OA being the most common form, affecting approximately 73% of individuals over 55 years. Traditional clinical assessments often fail to predict knee structural progression ac...

Explainable Versus Interpretable AI in Healthcare: How to Achieve Understanding.

Studies in health technology and informatics
The increasing adoption of deep learning methods has intensified the demand for explanations regarding how AI systems generate their results. This necessity originated primarily in the domain of image processing and has expanded to encompass the comp...

A Deep-Learning Framework for Ovarian Cancer Subtype Classification Using Whole Slide Images.

Studies in health technology and informatics
Ovarian cancer, a leading cause of cancer-related deaths among women, comprises distinct subtypes each requiring different treatment approaches. This paper presents a deep-learning framework for classifying ovarian cancer subtypes using Whole Slide I...

Energy-Efficient AI for Medical Diagnostics: Performance and Sustainability Analysis of ResNet and MobileNet.

Studies in health technology and informatics
Artificial intelligence (AI) has transformed medical diagnostics by enhancing the accuracy of disease detection, particularly through deep learning models to analyze medical imaging data. However, the energy demands of training these models, such as ...

Leveraging Vision Transformers in Multimodal Models for Retinal OCT Analysis.

Studies in health technology and informatics
Optical Coherence Tomography (OCT) has become an indispensable imaging modality in ophthalmology, providing high-resolution cross-sectional images of the retina. Accurate classification of OCT images is crucial for diagnosing retinal diseases such as...

Patient Survival Prediction by Analyzing Pathological Images of Patients After Liver Transplantation.

Studies in health technology and informatics
Predicting whether a patient will develop cancer using nuclear features on pathological images is important for decision making regarding patient treatment after liver transplantation or hepatectomy. Unlike manual segmentation to extract nuclei parts...

Challenging Black-Box Models: Interpretable Explanations for ECG Classification.

Studies in health technology and informatics
Deep learning methods achieve high performance, while often lacking explainability, hindering application in the field. We propose the use of a logistic regression classifier based on temporal aligned Electrocardiograms, and the utilisation of interp...

Bias Detection in Histology Images Using Explainable AI and Image Darkness Assessment.

Studies in health technology and informatics
The study underscores the importance of addressing biases in medical AI models to improve fairness, generalizability, and clinical utility. In this paper, we present a novel framework that combines Explainable AI (XAI) with image darkness assessment ...