AIMC Topic: Deep Learning

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Ensemble Deep Learning Model for Multicenter Classification of Thyroid Nodules on Ultrasound Images.

Medical science monitor : international medical journal of experimental and clinical research
BACKGROUND Thyroid nodules are extremely common and typically diagnosed with ultrasound whether benign or malignant. Imaging diagnosis assisted by Artificial Intelligence has attracted much attention in recent years. The aim of our study was to build...

A Deep Learning Model for Automated Sub-Basal Corneal Nerve Segmentation and Evaluation Using In Vivo Confocal Microscopy.

Translational vision science & technology
PURPOSE: The purpose of this study was to establish a deep learning model for automated sub-basal corneal nerve fiber (CNF) segmentation and evaluation with in vivo confocal microscopy (IVCM).

Deep learning detection of prostate cancer recurrence with F-FACBC (fluciclovine, Axumin®) positron emission tomography.

European journal of nuclear medicine and molecular imaging
PURPOSE: To evaluate the performance of deep learning (DL) classifiers in discriminating normal and abnormal F-FACBC (fluciclovine, Axumin®) PET scans based on the presence of tumor recurrence and/or metastases in patients with prostate cancer (PC) a...

Deep learning and cognitive science.

Cognition
In recent years, the family of algorithms collected under the term "deep learning" has revolutionized artificial intelligence, enabling machines to reach human-like performances in many complex cognitive tasks. Although deep learning models are groun...

Clinical-Grade Detection of Microsatellite Instability in Colorectal Tumors by Deep Learning.

Gastroenterology
BACKGROUND & AIMS: Microsatellite instability (MSI) and mismatch-repair deficiency (dMMR) in colorectal tumors are used to select treatment for patients. Deep learning can detect MSI and dMMR in tumor samples on routine histology slides faster and le...

Lower Adenoma Miss Rate of Computer-Aided Detection-Assisted Colonoscopy vs Routine White-Light Colonoscopy in a Prospective Tandem Study.

Gastroenterology
BACKGROUND AND AIMS: Up to 30% of adenomas might be missed during screening colonoscopy-these could be polyps that appear on-screen but are not recognized by endoscopists or polyps that are in locations that do not appear on the screen at all. Comput...

Development of Deep Learning Models for Predicting the Effects of Exposure to Engineered Nanomaterials on Daphnia magna.

Small (Weinheim an der Bergstrasse, Germany)
This study presents the results of applying deep learning methodologies within the ecotoxicology field, with the objective of training predictive models that can support hazard assessment and eventually the design of safer engineered nanomaterials (E...

Utilizing artificial intelligence in endoscopy: a clinician's guide.

Expert review of gastroenterology & hepatology
INTRODUCTION: Artificial intelligence (AI) that surpasses human ability in image recognition is expected to be applied in the field of gastrointestinal endoscopes. Accordingly, its research and development (R &D) is being actively conducted. With the...