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Showing 20 of 1,342 academic articles

Improving deep learning-based segmentation of diatoms in gigapixel-sized virtual slides by object-based tile positioning and object integrity constraint

Michael Kloster, Andrea M. Burfeid-Castellanos, Daniel Langenkämper, Tim W. Nattkemper et al. • PLoS ONE (2023)

Diatoms represent one of the morphologically and taxonomically most diverse groups of microscopic eukaryotes. Light microscopy-based taxonomic identification and enumeration of frustules, the silica shells of these microalgae, is broadly us...

DOAJ Open Access (DOAJ)

Enhancing Deep Learning–Based Subabdominal MR Image Segmentation During Rectal Cancer Treatment: Exploiting Multiscale Feature Pyramid Network and Bidirectional Cross-Attention Mechanism

Yu Xiao, Xin Yang, Sijuan Huang, Lihua Guo • International Journal of Biomedical Imaging (2025)

Conclusion: A multiscale feature pyramid network effectively reduces the semantic gap, and the bidirectional cross-attention mechanism facilitates feature alignment between the encoding and decoding stages....

DOAJ Open Access (DOAJ) DOI: 10.1155/ijbi/7560099

A Dual-Segmentation Framework for the Automatic Detection and Size Estimation of Shrimp

Malik Muhammad Waqar, Hassan Ali, Heng Zhou, Heba G. Mohamed et al. • Sensors (2025)

In shrimp farming, determining the physical traits of shrimp is vital for assessing their health and growth. One of the critical traits is their size, as it serves as a key indicator of growth rates, biomass, and effective feed management. ...

DOAJ Open Access (DOAJ) DOI: 10.3390/s25185830

Deep Learning in Microscopy Image Analysis: A Survey

Fuyong Xing, Yuanpu Xie, Hai Su, Fujun Liu et al. • IEEE Transactions on Neural Networks and Learning Systems (2017)

Computerized microscopy image analysis plays an important role in computer aided diagnosis and prognosis. Machine learning techniques have powered many aspects of medical investigation and clinical practice. Recently, deep learning is emerg...

OPENALEX DOI: https://doi.org/10.1109/tnnls.2017.2766168

Face recognition in dense crowd using deep learning approaches with IP camera

Sobhana Mummaneni, Venkata Chaitanya Satya Ramaraju Mudunuri, Sri Veerabhadra Vikas Bommaganti, Bhavya Vani Kalle et al. • Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska (2025)

A facial recognition system is a biometric security and surveillance system that can identify and monitor individuals in a crowded area. Manually monitoring a crowded environment is a difficult and error-prone task. Therefore, in such conte...

DOAJ Open Access (DOAJ) DOI: 10.35784/iapgos.6818

Fast reconstruction of SMS bSSFP myocardial perfusion images using noise map estimation network (NoiseMapNet): a head-to-head comparison with parallel imaging and iterative reconstruction

Naledi Lenah Adam, Grzegorz Kowalik, Andrew Tyler, Ronald Mooiweer et al. • Frontiers in Cardiovascular Medicine (2024)

BackgroundSimultaneous multi-slice (SMS) bSSFP imaging enables stress myocardial perfusion imaging with high spatial resolution and increased spatial coverage. Standard parallel imaging techniques (e.g., TGRAPPA) can be used for image recon...

DOAJ Open Access (DOAJ) DOI: 10.3389/fcvm.2024.1350345

Multimodal artificial intelligence for subepithelial lesion classification and characterization: a multicenter comparative study (with video)

Jiao Li, Xiaojuan Jing, Qin Zhang, Xiaoxiang Wang et al. • BMC Medical Informatics and Decision Making (2025)

Abstract Background Subepithelial lesions (SELs) present significant diagnostic challenges in gastrointestinal endoscopy, particularly in differentiating malignant types, such as gastrointestinal stromal tumors (GISTs) and neuroendocrine tu...

DOAJ Open Access (DOAJ) DOI: 10.1186/s12911-025-03147-9

Feasibility and effectiveness of automatic deep learning network and radiomics models for differentiating tumor stroma ratio in pancreatic ductal adenocarcinoma

Hongfan Liao, Jiang Yuan, Chunhua Liu, Jiao Zhang et al. • Insights into Imaging (2023)

Abstract Objective This study aims to compare the feasibility and effectiveness of automatic deep learning network and radiomics models in differentiating low tumor stroma ratio (TSR) from high TSR in pancreatic ductal adenocarcinoma (PDAC)...

DOAJ Open Access (DOAJ) DOI: 10.1186/s13244-023-01553-z

Machine learning and deep learning applications in microbiome research

Ricardo Hernández Medina, Svetlana Kutuzova, Knud Nor Nielsen, Joachim Johansen et al. • ISME Communications (2022)

The many microbial communities around us form interactive and dynamic ecosystems called microbiomes. Though concealed from the naked eye, microbiomes govern and influence macroscopic systems including human health, plant resilience, and bio...

OPENALEX cc-by DOI: https://doi.org/10.1038/s43705-022-00182-9 PMC: 9723725

A Review on Conventional Machine Learning vs Deep Learning

Nitin Kumar Chauhan, Krishna Pal Singh • International Conference on Computing, Power and Communication Technologies (GUCON) (2018)

In now days, deep learning has become a prominent and emerging research area in computer vision applications. Deep learning permits the multiple layers models for computation to learn representations of data by processing in their original ...

OPENALEX DOI: https://doi.org/10.1109/gucon.2018.8675097

Deep learning in holography and coherent imaging

Yair Rivenson, Yichen Wu, Aydogan Özcan • Light Science & Applications (2019)

Recent advances in deep learning have given rise to a new paradigm of holographic image reconstruction and phase recovery techniques with real-time performance. Through data-driven approaches, these emerging techniques have overcome some of...

OPENALEX cc-by DOI: https://doi.org/10.1038/s41377-019-0196-0 PMC: 6804620

Single-cell spatial metabolomics with cell-type specific protein profiling for tissue systems biology

Thomas Hu, Mayar Allam, Shuangyi Cai, Walter Henderson et al. • Nature Communications (2023)

Abstract Metabolic reprogramming in cancer and immune cells occurs to support their increasing energy needs in biological tissues. Here we propose Single Cell Spatially resolved Metabolic (scSpaMet) framework for joint protein-metabolite pr...

DOAJ Open Access (DOAJ) DOI: 10.1038/s41467-023-43917-5

Prediction of lymph node metastasis in papillary thyroid carcinoma using non-contrast CT-based radiomics and deep learning with thyroid lobe segmentation: A dual-center study

Hao Wang, Xuan Wang, Yusheng Du, You Wang et al. • European Journal of Radiology Open (2025)

Objectives: This study aimed to develop a predictive model for lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) patients by deep learning radiomic (DLRad) and clinical features. Methods: This study included 271 thyroid lobes...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.ejro.2025.100639