AIMC Topic: Humans

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Detection of malignant melanoma in H&E-stained images using deep learning techniques.

Tissue & cell
Histopathological images are widely used to diagnose diseases including skin cancer. As digital histopathological images are typically of very large size, in the order of several billion pixels, automated identification of all abnormal cell nuclei an...

Determining Top Fully Connected Layer's Hidden Neuron Count for Transfer Learning, Using Knowledge Distillation: a Case Study on Chest X-Ray Classification of Pneumonia and COVID-19.

Journal of digital imaging
Deep convolutional neural network (CNN)-assisted classification of images is one of the most discussed topics in recent years. Continuously innovation of neural network architectures is making it more correct and efficient every day. But training a n...

Artificial intelligence in the GPs office: a retrospective study on diagnostic accuracy.

Scandinavian journal of primary health care
OBJECTIVE: Machine learning (ML) is expected to play an increasing role within primary health care (PHC) in coming years. No peer-reviewed studies exist that evaluate the diagnostic accuracy of ML models compared to general practitioners (GPs). The a...

Diagnostic test accuracy of artificial intelligence analysis of cross-sectional imaging in pulmonary hypertension: a systematic literature review.

The British journal of radiology
OBJECTIVES: To undertake the first systematic review examining the performance of artificial intelligence (AI) applied to cross-sectional imaging for the diagnosis of acquired pulmonary arterial hypertension (PAH).

Comparison of different deep learning architectures for synthetic CT generation from MR images.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: Among the different available methods for synthetic CT generation from MR images for the task of MR-guided radiation planning, the deep learning algorithms have and do outperform their conventional counterparts. In this study, we investigate...

Perspectives: A surgeon's guide to machine learning.

International journal of surgery (London, England)
The exponential increase in the volume and complexity of healthcare data presents new challenges to researchers and clinicians in analysis and interpretation. The requirement for new strategies to extract meaningful information from large, noisy data...

Machine Learning Assisted Classification of Cell Lines and Cell States on Quantitative Phase Images.

Cells
In this report, we present implementation and validation of machine-learning classifiers for distinguishing between cell types (HeLa, A549, 3T3 cell lines) and states (live, necrosis, apoptosis) based on the analysis of optical parameters derived fro...

Porting Rulex Software to the Raspberry Pi for Machine Learning Applications on the Edge.

Sensors (Basel, Switzerland)
Edge Computing enables to perform measurement and cognitive decisions outside a central server by performing data storage, manipulation, and processing on the Internet of Things (IoT) node. Also, Artificial Intelligence (AI) and Machine Learning appl...

A Class-Imbalanced Deep Learning Fall Detection Algorithm Using Wearable Sensors.

Sensors (Basel, Switzerland)
Falling represents one of the most serious health risks for elderly people; it may cause irreversible injuries if the individual cannot obtain timely treatment after the fall happens. Therefore, timely and accurate fall detection algorithm research i...