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Classification of subtypes including LCNEC in lung cancer biopsy slides using convolutional neural network from scratch.

Scientific reports
Identifying the lung carcinoma subtype in small biopsy specimens is an important part of determining a suitable treatment plan but is often challenging without the help of special and/or immunohistochemical stains. Pathology image analysis that tackl...

Robust and generalizable embryo selection based on artificial intelligence and time-lapse image sequences.

PloS one
Assessing and selecting the most viable embryos for transfer is an essential part of in vitro fertilization (IVF). In recent years, several approaches have been made to improve and automate the procedure using artificial intelligence (AI) and deep le...

Hybrid feature selection-based machine learning Classification system for the prediction of injury severity in single and multiple-vehicle accidents.

PloS one
To undertake a reliable analysis of injury severity in road traffic accidents, a complete understanding of important attributes is essential. As a result of the shift from traditional statistical parametric procedures to computer-aided methods, machi...

External Validation of a Laboratory Prediction Algorithm for the Reduction of Unnecessary Labs in the Critical Care Setting.

The American journal of medicine
BACKGROUND: Unnecessary laboratory tests contribute to iatrogenic harm and are a major source of waste in the health care system. We previously developed a machine learning algorithm to help clinicians identify unnecessary laboratory tests, but it ha...

Account of Deep Learning-Based Ultrasonic Image Feature in the Diagnosis of Severe Sepsis Complicated with Acute Kidney Injury.

Computational and mathematical methods in medicine
This study was aimed at analyzing the diagnostic value of convolutional neural network models on account of deep learning for severe sepsis complicated with acute kidney injury and providing an effective theoretical reference for the clinical use of ...

An improved X-means and isolation forest based methodology for network traffic anomaly detection.

PloS one
Anomaly detection in network traffic is becoming a challenging task due to the complexity of large-scale networks and the proliferation of various social network applications. In the actual industrial environment, only recently obtained unlabelled da...

Multiple instance learning detects peripheral arterial disease from high-resolution color fundus photography.

Scientific reports
Peripheral arterial disease (PAD) is caused by atherosclerosis and is a common disease of the elderly leading to excess morbidity and mortality. Early PAD diagnosis is important, as the only available causal therapy is addressing risk factors like sm...

A deep learning model for breast ductal carcinoma in situ classification in whole slide images.

Virchows Archiv : an international journal of pathology
The pathological differential diagnosis between breast ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC) is of pivotal importance for determining optimum cancer treatment(s) and clinical outcomes. Since conventional diagnosis by pat...

A data-driven ultrasound approach discriminates pathological high grade prostate cancer.

Scientific reports
Accurate prostate cancer screening is imperative for reducing the risk of cancer death. Ultrasound imaging, although easy, tends to have low resolution and high inter-observer variability. Here, we show that our integrated machine learning approach e...