AIMC Topic: Deep Learning

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A multimodal dataset for training deep learning models aimed at detecting and analyzing sleep apnea.

Scientific data
Sleep Apnea Syndrome (SAS) is a serious respiratory disorder that can lead to a range of complications, including hypertension, arrhythmias, cognitive impair- ment, and metabolic disturbances. Due to the insidious nature of its symptoms, patients oft...

Development of a clinical decision support system for breast cancer detection using ensemble deep learning.

Scientific reports
Advancements in diagnostic technology are required to improve patient outcomes and facilitate early diagnosis, as breast cancer is a substantial global health concern. This research discusses the creation of a unique Deep Learning (DL) Ensemble Deep ...

Bering: joint cell segmentation and annotation for spatial transcriptomics with transferred graph embeddings.

Nature communications
Single-cell spatial transcriptomics can provide subcellular resolution for a deep understanding of molecular mechanisms. However, accurate segmentation and annotation remain a major challenge that limits downstream analysis. Current machine learning ...

Curating a knowledge base for patients with neurosyphilis: a study protocol of a DEep learning Framework for pErsonalized prediction of Adverse prognosTic events in NeuroSyphilis (DEFEAT-NS).

BMJ open
INTRODUCTION: Adverse prognostic events (APE) of neurosyphilis include ongoing syphilitic meningitis, meningovascular syphilis, parenchymatous neurosyphilis and death. Its complexity and rarity have the potential to result in the underestimated true ...

AI-powered skin spectral imaging enables instant sepsis diagnosis and outcome prediction in critically ill patients.

Science advances
With sepsis remaining a leading cause of mortality, early identification of patients with sepsis and those at high risk of death is a challenge of high socioeconomic importance. Given the potential of hyperspectral imaging (HSI) to monitor microcircu...

Synergistic fusion: An integrated pipeline of CLAHE, YOLO models, and advanced super-resolution for enhanced thermal eye detection.

PloS one
Accurate eye detection in thermal images is essential for diverse applications, including biometrics, healthcare, driver monitoring, and human-computer interaction. However, achieving this accuracy is often hindered by the inherent limitations of the...

Deep learning-based screening approach for priority pollutants: a case study on retired power battery recycling.

Environmental pollution (Barking, Essex : 1987)
With the rapid increase in the production of retired power batteries, the potential environmental risks during recycling must urgently be identified and assessed. This study presented a novel screening framework for pollutant prioritization utilizing...

A cascade approach for the early detection and localization of myocardial infarction in 2D-echocardiography.

Medical engineering & physics
Myocardial infarction (MI) detection and localization through echocardiography are crucial for effective patient management. However, current diagnostic approaches rely heavily on visual assessment, which can be subjective. In this work we developed ...

Reflections on dynamic prediction of Alzheimer's disease: advancements in modeling longitudinal outcomes and time-to-event data.

BMC medical research methodology
BACKGROUND: Individualized prediction of health outcomes supports clinical medicine and decision making. Our primary objective was to offer a comprehensive survey of methods for the dynamic prediction of Alzheimer's disease (AD), encompassing both co...

Integrative habitat analysis and multi-instance deep learning for predictive model of PD-1/PD-L1 immunotherapy efficacy in NSCLC patients: a dual-center retrospective study.

BMC medical imaging
BACKGROUND: PD-1/PD-L1 immunotherapy represents the primary treatment for advanced NSCLC patients; however, response rates to this therapy vary among individuals. This dual-center study aimed to integrate habitat radiomics and multi-instance deep lea...