AIMC Topic: Deep Learning

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Semiquantitative Fingerprinting Based on Pseudotargeted Metabolomics and Deep Learning for the Identification of and Its Major Serotypes.

Analytical chemistry
The rapid identification of pathogenic microorganism serotypes is still a bottleneck problem to be solved urgently. Compared with proteomics technology, metabolomics technology is directly related to phenotypes and has higher specificity in identifyi...

Stratified assessment of an FDA-cleared deep learning algorithm for automated detection and contouring of metastatic brain tumors in stereotactic radiosurgery.

Radiation oncology (London, England)
PURPOSE: Artificial intelligence-based tools can be leveraged to improve detection and segmentation of brain metastases for stereotactic radiosurgery (SRS). VBrain by Vysioneer Inc. is a deep learning algorithm with recent FDA clearance to assist in ...

Remote assessment of cognition and quality of life following radiotherapy for glioma: deep-learning-based predictive models and MRI correlates.

Journal of neuro-oncology
BACKGROUND: Glioma irradiation often unavoidably damages the brain volume and affects cognition. This study aims to evaluate the relationship of remote cognitive assessments in determining cognitive impairment of irradiated glioma patients in relatio...

A study on the optimal condition of ground truth area for liver tumor detection in ultrasound images using deep learning.

Journal of medical ultrasonics (2001)
PURPOSE: In recent years, efforts to apply artificial intelligence (AI) to the medical field have been growing. In general, a vast amount of high-quality training data is necessary to make great AI. For tumor detection AI, annotation quality is impor...

LightSeizureNet: A Lightweight Deep Learning Model for Real-Time Epileptic Seizure Detection.

IEEE journal of biomedical and health informatics
The monitoring of epilepsy patients in non-hospital environment is highly desirable, where ultra-low power wearable seizure detection devices are essential in such a system. The state-of-the-art epileptic seizure detection algorithms targeting such d...

High-resolution rural poverty mapping in Pakistan with ensemble deep learning.

PloS one
High resolution poverty mapping supports evidence-based policy and research, yet about half of all countries lack the survey data needed to generate useful poverty maps. To overcome this challenge, new non-traditional data sources and deep learning t...

BCR-Net: A deep learning framework to predict breast cancer recurrence from histopathology images.

PloS one
Breast cancer is the most common malignancy in women, with over 40,000 deaths annually in the United States alone. Clinicians often rely on the breast cancer recurrence score, Oncotype DX (ODX), for risk stratification of breast cancer patients, by u...

Remote assessment of cognition and quality of life following radiotherapy for nasopharyngeal carcinoma: deep-learning-based predictive models and MRI correlates.

Journal of cancer survivorship : research and practice
PURPOSE: Irradiation of the brain regions from nasopharyngeal carcinoma (NPC) radiotherapy (RT) is frequently unavoidable, which may result in radiation-induced cognitive deficit. Using deep learning (DL), the study aims to develop prediction models ...

Pushing the limits of low-cost ultra-low-field MRI by dual-acquisition deep learning 3D superresolution.

Magnetic resonance in medicine
PURPOSE: Recent development of ultra-low-field (ULF) MRI presents opportunities for low-power, shielding-free, and portable clinical applications at a fraction of the cost. However, its performance remains limited by poor image quality. Here, a compu...

Synthetic CT generation from CBCT using double-chain-CycleGAN.

Computers in biology and medicine
PURPOSE: Cone-beam CT (CBCT) has the advantage of being less expensive, lower radiation dose, less harm to patients, and higher spatial resolution. However, noticeable noise and defects, such as bone and metal artifacts, limit its clinical applicatio...