AIMC Topic: Deep Learning

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Imatinib adherence prediction using machine learning approach in patients with gastrointestinal stromal tumor.

Cancer
BACKGROUND: Nonadherence to imatinib is common in patients with gastrointestinal stromal tumor (GIST), which is associated with poor prognosis and financial burden. The primary aim of this study was to investigate the adherence rate in patients with ...

MMGCN: Multi-modal multi-view graph convolutional networks for cancer prognosis prediction.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Accurate prognosis prediction for cancer patients plays a significant role in the formulation of treatment strategies, considerably impacting personalized medicine. Recent advancements in this field indicate that integrating...

Near-optimal deep neural network approximation for Korobov functions with respect to L and H norms.

Neural networks : the official journal of the International Neural Network Society
This paper derives the optimal rate of approximation for Korobov functions with deep neural networks in the high dimensional hypercube with respect to L-norms and H-norm. Our approximation bounds are non-asymptotic in both the width and depth of the ...

Deep Learning Based Shear Wave Detection and Segmentation Tool for Use in Point-of-Care for Chronic Liver Disease Assessments.

Ultrasound in medicine & biology
OBJECTIVE: As metabolic dysfunction-associated steatotic liver disease (MASLD) becomes more prevalent worldwide, it is imperative to create more accurate technologies that make it easy to assess the liver in a point-of-care setting. The aim of this s...

Topology-preserving segmentation of abdominal muscle layers from ultrasound images.

Medical physics
BACKGROUND: In clinical anesthesia, precise segmentation of muscle layers from abdominal ultrasound images is crucial for identifying nerve block locations accurately. Despite deep learning advancements, challenges persist in segmenting muscle layers...

Molecular designing of potential environmentally friendly PFAS based on deep learning and generative models.

The Science of the total environment
Perfluoroalkyl and polyfluoroalkyl substances (PFAS) are widely used across a spectrum of industrial and consumer goods. Nonetheless, their persistent nature and tendency to accumulate in biological systems pose substantial environmental and health t...

Clinical implementation of deep learning robust IMPT planning in oropharyngeal cancer patients: A blinded clinical study.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: This study aimed to evaluate the plan quality of our deep learning-based automated treatment planning method for robustly optimized intensity-modulated proton therapy (IMPT) plans in patients with oropharyngeal carcinoma (OPC)...

Interpretable Fine-Grained Phenotypes of Subcellular Dynamics via Unsupervised Deep Learning.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Uncovering fine-grained phenotypes of live cell dynamics is pivotal for a comprehensive understanding of the heterogeneity in healthy and diseased biological processes. However, this endeavor poses significant technical challenges for unsupervised ma...

Artificial Intelligence in Head and Neck Cancer: Innovations, Applications, and Future Directions.

Current oncology (Toronto, Ont.)
Artificial intelligence (AI) is revolutionizing head and neck cancer (HNC) care by providing innovative tools that enhance diagnostic accuracy and personalize treatment strategies. This review highlights the advancements in AI technologies, including...

A Combined CNN Architecture for Speech Emotion Recognition.

Sensors (Basel, Switzerland)
Emotion recognition through speech is a technique employed in various scenarios of Human-Computer Interaction (HCI). Existing approaches have achieved significant results; however, limitations persist, with the quantity and diversity of data being mo...