AIMC Topic: Deep Learning

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Gaussian random fields as an abstract representation of patient metadata for multimodal medical image segmentation.

Scientific reports
Growing rates of chronic wound occurrence, especially in patients with diabetes, has become a recent concerning trend. Chronic wounds are difficult and costly to treat, and have become a serious burden on health care systems worldwide. Innovative dee...

An efficient dual-branch framework via implicit self-texture enhancement for arbitrary-scale histopathology image super-resolution.

Scientific reports
High-quality whole-slide scanning is expensive, complex, and time-consuming, thus limiting the acquisition and utilization of high-resolution histopathology images in daily clinical work. Deep learning-based single-image super-resolution (SISR) techn...

Interpretable deep learning for gastric cancer detection: a fusion of AI architectures and explainability analysis.

Frontiers in immunology
INTRODUCTION: The rise in cases of Gastric Cancer has increased in recent times and demands accurate and timely detection to improve patients' well-being. The traditional cancer detection techniques face issues of explainability and precision posing ...

Deep learning for predicting invasive recurrence of ductal carcinoma in situ: leveraging histopathology images and clinical features.

EBioMedicine
BACKGROUND: Ductal Carcinoma In Situ (DCIS) can progress to ipsilateral invasive breast cancer (IBC) but over 75% of DCIS lesions do not progress if untreated. Currently, DCIS that might progress to IBC cannot reliably be identified. Therefore, most ...

Personalized prediction of gait freezing using dynamic mode decomposition.

Scientific reports
Freezing of gait (FoG) is a common severe gait disorder in patients with advanced Parkinson's disease. The ability to predict the onset of FoG episodes early on allows for timely intervention, which is essential for improving the life quality of pati...

Drug-drug interaction prediction of traditional Chinese medicine based on graph attention networks.

Scientific reports
Predicting drug-drug interactions (DDI) is crucial for preventing adverse reactions in patients and plays a vital role in drug design and development. However, traditional Chinese medicine (TCM) formulations, typically composed of multiple herbal ing...

Large Scale MRI Collection and Segmentation of Cirrhotic Liver.

Scientific data
Liver cirrhosis represents the end stage of chronic liver disease, characterized by extensive fibrosis and nodular regeneration that significantly increases mortality risk. While magnetic resonance imaging (MRI) offers a non-invasive assessment, accu...

A Novel Artificial Intelligence Approach to Kennedy Classification for Partially Edentulous Patients Using Panoramic Radiographs.

The European journal of prosthodontics and restorative dentistry
OBJECTIVES: This study aimed to develop an artificial intelligence system for automated classification of partially edentulous arches from panoramic radiographs using the Kennedy classification system and Applegate's rules, alongside identifying exis...

RETINA: Reconstruction-based pre-trained enhanced TransUNet for electron microscopy segmentation on the CEM500K dataset.

PLoS computational biology
Electron microscopy (EM) has revolutionized our understanding of cellular structures at the nanoscale. Accurate image segmentation is required for analyzing EM images. While manual segmentation is reliable, it is labor-intensive, incentivizing the de...

Advanced predictive modeling for enhanced mortality prediction in ICU stroke patients using clinical data.

PloS one
Background Stroke is second-leading cause of disability and death among adults. Approximately 17 million people suffer from a stroke annually, with about 85% being ischemic strokes. Predicting mortality of ischemic stroke patients in intensive care u...