AIMC Topic: Benchmarking

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Breast Cancer Histopathological Images Segmentation Using Deep Learning.

Sensors (Basel, Switzerland)
Hospitals generate a significant amount of medical data every day, which constitute a very rich database for research. Today, this database is still not exploitable because to make its valorization possible, the images require an annotation which rem...

A Novel Two-Stage Induced Deep Learning System for Classifying Similar Drugs with Diverse Packaging.

Sensors (Basel, Switzerland)
Dispensing errors play a crucial role in various medical errors, unfortunately emerging as the third leading cause of death in the United States. This alarming statistic has spurred the World Health Organization (WHO) into action, leading to the init...

Deep learning-based dose map prediction for high-dose-rate brachytherapy.

Physics in medicine and biology
. Creating a clinically acceptable plan in the time-sensitive clinic workflow of brachytherapy is challenging. Deep learning-based dose prediction techniques have been reported as promising solutions with high efficiency and accuracy. However, curren...

Technical skill assessment in minimally invasive surgery using artificial intelligence: a systematic review.

Surgical endoscopy
BACKGROUND: Technical skill assessment in surgery relies on expert opinion. Therefore, it is time-consuming, costly, and often lacks objectivity. Analysis of intraoperative data by artificial intelligence (AI) has the potential for automated technica...

MedTric : A clinically applicable metric for evaluation of multi-label computational diagnostic systems.

PloS one
When judging the quality of a computational system for a pathological screening task, several factors seem to be important, like sensitivity, specificity, accuracy, etc. With machine learning based approaches showing promise in the multi-label paradi...

Post-Processing Fairness Evaluation of Federated Models: An Unsupervised Approach in Healthcare.

IEEE/ACM transactions on computational biology and bioinformatics
Modern Healthcare cyberphysical systems have begun to rely more and more on distributed AI leveraging the power of Federated Learning (FL). Its ability to train Machine Learning (ML) and Deep Learning (DL) models for the wide variety of medical field...

Explanations as a New Metric for Feature Selection: A Systematic Approach.

IEEE journal of biomedical and health informatics
With the extensive use of Machine Learning (ML) in the biomedical field, there was an increasing need for Explainable Artificial Intelligence (XAI) to improve transparency and reveal complex hidden relationships between variables for medical practiti...

Characterization of Synthetic Health Data Using Rule-Based Artificial Intelligence Models.

IEEE journal of biomedical and health informatics
The aim of this study is to apply and characterize eXplainable AI (XAI) to assess the quality of synthetic health data generated using a data augmentation algorithm. In this exploratory study, several synthetic datasets are generated using various co...

Physiologically-Informed Gaussian Processes for Interpretable Modelling of Psycho-Physiological States.

IEEE journal of biomedical and health informatics
The widespread popularity of Machine Learning (ML) models in healthcare solutions has increased the demand for their interpretability and accountability. In this paper, we propose the Physiologically-Informed Gaussian Process (PhGP) classification mo...

Attention-based multimodal fusion with contrast for robust clinical prediction in the face of missing modalities.

Journal of biomedical informatics
OBJECTIVE: With the increasing amount and growing variety of healthcare data, multimodal machine learning supporting integrated modeling of structured and unstructured data is an increasingly important tool for clinical machine learning tasks. Howeve...