AIMC Topic: Deep Learning

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Detection of dental caries under fixed dental prostheses by analyzing digital panoramic radiographs with artificial intelligence algorithms based on deep learning methods.

BMC oral health
BACKGROUND: The aim of this study was to evaluate the efficacy of detecting dental caries under fixed dental prostheses (FDPs) through the analysis of panoramic radiographs utilizing convolutional neural network (CNN) based You Only Look Once (YOLO) ...

Development of a deep learning system for predicting biochemical recurrence in prostate cancer.

BMC cancer
BACKGROUND: Biochemical recurrence (BCR) occurs in 20%-40% of men with prostate cancer (PCa) who undergo radical prostatectomy. Predicting which patients will experience BCR in advance helps in formulating more targeted prostatectomy procedures. Howe...

A deep learning-based system for automatic detection of emesis with high accuracy in Suncus murinus.

Communications biology
Quantifying emesis in Suncus murinus (S. murinus) has traditionally relied on direct observation or reviewing recorded behaviour, which are laborious, time-consuming processes that are susceptible to operator error. With rapid advancements in deep le...

A robust deep learning framework for multiclass skin cancer classification.

Scientific reports
Skin cancer represents a significant global health concern, where early and precise diagnosis plays a pivotal role in improving treatment efficacy and patient survival rates. Nonetheless, the inherent visual similarities between benign and malignant ...

Identifying invasiveness to aid lung adenocarcinoma diagnosis using deep learning and pathomics.

Scientific reports
Most classification efforts for primary subtypes of lung adenocarcinoma (LUAD) have not yet been integrated into clinical practice. This study explores the feasibility of combining deep learning and pathomics to identify tumor invasiveness in LUAD pa...

Human sleep position classification using a lightweight model and acceleration data.

Sleep & breathing = Schlaf & Atmung
PURPOSE: This exploratory study introduces a portable, wearable device using a single accelerometer to monitor twelve sleep positions. Targeted for home use, the device aims to assist patients with mild conditions such as gastroesophageal reflux dise...

PhysioEx: a new Python library for explainable sleep staging through deep learning.

Physiological measurement
Sleep staging is a crucial task in clinical and research contexts for diagnosing and understanding sleep disorders. This work introduces PhysioEx (Physiological Signal Explainer), a Python library designed to support the analysis of sleep stages usin...

Scaling Synthetic Brain Data Generation.

IEEE journal of biomedical and health informatics
The limited availability of diverse, high-quality datasets is a significant challenge in applying deep learning to neuroimaging research. Although synthetic data generation can potentially address this issue, on-the-fly generation is computationally ...

WaveSleepNet: An Interpretable Network for Expert-Like Sleep Staging.

IEEE journal of biomedical and health informatics
Although deep learning algorithms have proven their efficiency in automatic sleep staging, their "black-box" nature has limited their clinical adoption. In this study, we propose WaveSleepNet, an interpretable neural network for sleep staging that re...

EDSRNet: An Enhanced Decoder Semantic Recovery Network for 2D Medical Image Segmentation.

IEEE journal of biomedical and health informatics
In recent years, with the advancement of medical imaging technology, medical image segmentation has played a key role in assisting diagnosis and treatment planning. Current deep learning-based medical image segmentation methods mainly adopt encoder-d...