AI Medical Compendium Journal:
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Showing 101 to 110 of 148 articles

The Dresden Surgical Anatomy Dataset for Abdominal Organ Segmentation in Surgical Data Science.

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Laparoscopy is an imaging technique that enables minimally-invasive procedures in various medical disciplines including abdominal surgery, gynaecology and urology. To date, publicly available laparoscopic image datasets are mostly limited to general ...

Image dataset for benchmarking automated fish detection and classification algorithms.

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Multiparametric video-cabled marine observatories are becoming strategic to monitor remotely and in real-time the marine ecosystem. Those platforms can achieve continuous, high-frequency and long-lasting image data sets that require automation in ord...

N-Omniglot, a large-scale neuromorphic dataset for spatio-temporal sparse few-shot learning.

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Few-shot learning (learning with a few samples) is one of the most important cognitive abilities of the human brain. However, the current artificial intelligence systems meet difficulties in achieving this ability. Similar challenges also exist for b...

The Health Gym: synthetic health-related datasets for the development of reinforcement learning algorithms.

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In recent years, the machine learning research community has benefited tremendously from the availability of openly accessible benchmark datasets. Clinical data are usually not openly available due to their confidential nature. This has hampered the ...

Human mobile robot interaction in the retail environment.

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As technology advances, Human-Robot Interaction (HRI) is boosting overall system efficiency and productivity. However, allowing robots to be present closely with humans will inevitably put higher demands on precise human motion tracking and predictio...

Pan-tumor CAnine cuTaneous Cancer Histology (CATCH) dataset.

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Due to morphological similarities, the differentiation of histologic sections of cutaneous tumors into individual subtypes can be challenging. Recently, deep learning-based approaches have proven their potential for supporting pathologists in this re...

Inflation of test accuracy due to data leakage in deep learning-based classification of OCT images.

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In the application of deep learning on optical coherence tomography (OCT) data, it is common to train classification networks using 2D images originating from volumetric data. Given the micrometer resolution of OCT systems, consecutive images are oft...

An annotated image dataset of medically and forensically important flies for deep learning model training.

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Conventional methods to study insect taxonomy especially forensic and medical dipterous flies are often tedious, time-consuming, labor-intensive, and expensive. An automated recognition system with image processing and computer vision provides an exc...

The Leaf Clinical Trials Corpus: a new resource for query generation from clinical trial eligibility criteria.

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Identifying cohorts of patients based on eligibility criteria such as medical conditions, procedures, and medication use is critical to recruitment for clinical trials. Such criteria are often most naturally described in free-text, using language fam...

BRAX, Brazilian labeled chest x-ray dataset.

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Chest radiographs allow for the meticulous examination of a patient's chest but demands specialized training for proper interpretation. Automated analysis of medical imaging has become increasingly accessible with the advent of machine learning (ML) ...