AIMC Topic: Deep Learning

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A deep learning-based method for the detection and segmentation of breast masses in ultrasound images.

Physics in medicine and biology
Automated detection and segmentation of breast masses in ultrasound images are critical for breast cancer diagnosis, but remain challenging due to limited image quality and complex breast tissues. This study aims to develop a deep learning-based meth...

Endoscopic Artificial Intelligence for Image Analysis in Gastrointestinal Neoplasms.

Digestion
BACKGROUND: Artificial intelligence (AI) using deep learning systems has recently been utilized in various medical fields. In the field of gastroenterology, AI is primarily implemented in image recognition and utilized in the realm of gastrointestina...

Deep learning-based material decomposition of iodine and calcium in mobile photon counting detector CT.

PloS one
Photon-counting detector (PCD)-based computed tomography (CT) offers several advantages over conventional energy-integrating detector-based CT. Among them, the ability to discriminate energy exhibits significant potential for clinical applications be...

Deep learning-based respiratory muscle segmentation as a potential imaging biomarker for respiratory function assessment.

PloS one
Respiratory diseases significantly affect respiratory function, making them a considerable contributor to global mortality. The respiratory muscles play an important role in disease prognosis; as such, quantitative analysis of the respiratory muscles...

DeepDRA: Drug repurposing using multi-omics data integration with autoencoders.

PloS one
Cancer treatment has become one of the biggest challenges in the world today. Different treatments are used against cancer; drug-based treatments have shown better results. On the other hand, designing new drugs for cancer is costly and time-consumin...

MEFFNet: Forecasting Myoelectric Indices of Muscle Fatigue in Healthy and Post-Stroke During Voluntary and FES-Induced Dynamic Contractions.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Myoelectric indices forecasting is important for muscle fatigue monitoring in wearable technologies, adaptive control of assistive devices like exoskeletons and prostheses, functional electrical stimulation (FES)-based Neuroprostheses, and more. Non-...

Impact of Downsampling Size and Interpretation Methods on Diagnostic Accuracy in Deep Learning Model for Breast Cancer Using Digital Breast Tomosynthesis Images.

The Tohoku journal of experimental medicine
While deep learning (DL) models have shown promise in breast cancer diagnosis using digital breast tomosynthesis (DBT) images, the impact of varying matrix sizes and image interpolation methods on diagnostic accuracy remains unclear. Understanding th...

Deep Learning Model Using Stool Pictures for Predicting Endoscopic Mucosal Inflammation in Patients With Ulcerative Colitis.

The American journal of gastroenterology
INTRODUCTION: Stool characteristics may change depending on the endoscopic activity of ulcerative colitis (UC). We developed a deep learning model using stool photographs of patients with UC (DLSUC) to predict endoscopic mucosal inflammation.

Precise Localization for Anatomo-Physiological Hallmarks of the Cervical Spine by Using Neural Memory Ordinary Differential Equation.

International journal of neural systems
In the evaluation of cervical spine disorders, precise positioning of anatomo-physiological hallmarks is fundamental for calculating diverse measurement metrics. Despite the fact that deep learning has achieved impressive results in the field of keyp...

Integrating multi-task and cost-sensitive learning for predicting mortality risk of chronic diseases in the elderly using real-world data.

International journal of medical informatics
BACKGROUND AND OBJECTIVE: Real-world data encompass population diversity, enabling insights into chronic disease mortality risk among the elderly. Deep learning excels on large datasets, offering promise for real-world data. However, current models f...