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Showing 20 of 158 academic articles

Machine and deep learning methods for radiomics

Michele Avanzo, Lise Wei, Joseph Stancanello, Martin Vallières et al. • Medical Physics (2020)

Radiomics is an emerging area in quantitative image analysis that aims to relate large-scale extracted imaging information to clinical and biological endpoints. The development of quantitative imaging methods along with machine learning has...

OPENALEX other-oa DOI: https://doi.org/10.1002/mp.13678 PMC: 8965689

SPAF-SegNeXt: Prior-Guided Attention and Frequency-Spatial Hybrid Fusion for Multimodal Sugarcane Segmentation in Remote Sensing

Wei Zhang, Chao Ren, Haoming Bai, Huada Huang et al. • IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2026)

Accurate monitoring of the spatial extent of sugarcane cultivation is critical for optimizing land-use efficiency and regulating the sugar industry. However, sugarcane cultivation areas are characterized by complex topography and fragmented...

DOAJ Open Access (DOAJ) DOI: 10.1109/JSTARS.2026.3716367

Deep Learning for the Earth Sciences

Gustau Camps‐Valls, Devis Tuia, Xiao Xiang Zhu, Markus Reichstein • Wiley eBooks (2021)

Deep learning is a fundamental technique in modern Artificial Intelligence and is being applied to disciplines across the scientific spectrum; earth science is no exception. Yet, the link between deep learning and Earth sciences has only re...

OPENALEX Open Access DOI: https://doi.org/10.1002/9781119646181

A 2D and 3D segmentation-based microstructure study on the role of brittle phases in diffusion brazed AISI 304L/NiCrSiFeMoB joints

Johannes L. Otto, Lukas M. Sauer, Malte Brink, Thorge Schaum et al. • Materials & Design (2023)

Nickel-based filler metals are frequently used in high temperature vacuum diffusion brazing for austenitic stainless-steel joints when components are subjected to high static or dynamic loads, corrosive environments and elevated temperature...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.matdes.2023.112401

Estimating Uncertainty of Geographic Atrophy Segmentations with Bayesian Deep Learning

Theodore Spaide, PhD, Anand E. Rajesh, MD, Nayoon Gim, Marian Blazes, MD et al. • Ophthalmology Science (2025)

Purpose: To apply methods for quantifying uncertainty of deep learning segmentation of geographic atrophy (GA). Design: Retrospective analysis of OCT images and model comparison. Participants: One hundred twenty-six eyes from 87 participant...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.xops.2024.100587

A deep learning model for the localization and extraction of brain tumors from MR images using YOLOv7 and grab cut algorithm

Srigiri Krishnapriya, Yepuganti Karuna • Frontiers in Oncology (2024)

IntroductionBrain tumors are a common disease that affects millions of people worldwide. Considering the severity of brain tumors (BT), it is important to diagnose the disease in its early stages. With advancements in the diagnostic process...

DOAJ Open Access (DOAJ) DOI: 10.3389/fonc.2024.1347363

Deep Learning in Medical Image Analysis

Dinggang G. Shen, Guorong Wu A, Heung‐Il Suk • Annual Review of Biomedical Engineering (2017)

This review covers computer-assisted analysis of images in the field of medical imaging. Recent advances in machine learning, especially with regard to deep learning, are helping to identify, classify, and quantify patterns in medical image...

OPENALEX Open Access DOI: https://doi.org/10.1146/annurev-bioeng-071516-044442 PMC: 5479722

Deep Learning: A Primer for Radiologists

Gabriel Chartrand, Phillip M. Cheng, Eugene Vorontsov, Michal Drozdzal et al. • Radiographics (2017)

Deep learning is a class of machine learning methods that are gaining success and attracting interest in many domains, including computer vision, speech recognition, natural language processing, and playing games. Deep learning methods prod...

OPENALEX DOI: https://doi.org/10.1148/rg.2017170077

Weed Detection from Unmanned Aerial Vehicle Imagery Using Deep Learning—A Comparison between High-End and Low-Cost Multispectral Sensors

Anna Teresa Seiche, Lucas Wittstruck, Thomas Jarmer • Sensors (2024)

In order to meet the increasing demand for crops under challenging climate conditions, efficient and sustainable cultivation strategies are becoming essential in agriculture. Targeted herbicide use reduces environmental pollution and effect...

DOAJ Open Access (DOAJ) DOI: 10.3390/s24051544

Automated segmentation of brain metastases in T1-weighted contrast-enhanced MR images pre and post stereotactic radiosurgery

Hemalatha Kanakarajan, Wouter De Baene, Patrick Hanssens, Margriet Sitskoorn • BMC Medical Imaging (2025)

Abstract Background and purpose Accurate segmentation of brain metastases on Magnetic Resonance Imaging (MRI) is tedious and time-consuming for radiologists that could be optimized with deep learning (DL). Previous studies assessed several ...

DOAJ Open Access (DOAJ) DOI: 10.1186/s12880-025-01643-y

Cyclic multiplex fluorescent immunohistochemistry and machine learning reveal distinct states of astrocytes and microglia in normal aging and Alzheimer’s disease

Clara Muñoz-Castro, Ayush Noori, Colin G. Magdamo, Zhaozhi Li et al. • Journal of Neuroinflammation (2022)

Abstract Background Astrocytes and microglia react to Aβ plaques, neurofibrillary tangles, and neurodegeneration in the Alzheimer’s disease (AD) brain. Single-nuclei and single-cell RNA-seq have revealed multiple states or subpopulations of...

DOAJ Open Access (DOAJ) DOI: 10.1186/s12974-022-02383-4

Machine learning in breast MRI

Beatriu Reig, Laura C. Heacock, Krzysztof J. Geras, Linda P. Moy • Journal of Magnetic Resonance Imaging (2019)

Machine-learning techniques have led to remarkable advances in data extraction and analysis of medical imaging. Applications of machine learning to breast MRI continue to expand rapidly as increasingly accurate 3D breast and lesion segmenta...

OPENALEX DOI: https://doi.org/10.1002/jmri.26852 PMC: 7085409

Efficient covid19 disease diagnosis based on cough signal processing and supervised machine learning

Khaled Bensid, Abdelhai Lati, Azeddine Benlamoudi, Brahim Elkhalil Ghouar et al. • Diagnostyka (2022)

The spread of the coronavirus has claimed the lives of millions around the world, which led to the emergence of an economic and health crisis at the global level, which prompted many researchers to submit proposals for early diagnosis of th...

DOAJ Open Access (DOAJ) DOI: 10.29354/diag/156751

Analysis of applying machine learning methods in rock classification problems on core samples

Alexander I. Kochegurov, Vladislav I. Denisov, Elizaveta A. Zadorozhnykh • Известия Томского политехнического университета: Инжиниринг георесурсов (2024)

Relevance. Core studies in the oil and gas industry allow us to obtain some filtration and capacity characteristics of rocks, as well as to give an idea of the composition and structure of the subsoil. This information is extremely importan...

DOAJ Open Access (DOAJ) DOI: 10.18799/24131830/2024/9/4792