AIMC Topic: Machine Learning

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Serum microRNA panel excavated by machine learning as a potential biomarker for the detection of gastric cancer.

Oncology reports
Early detection of gastric cancer (GC) is crucial to improve the therapeutic effect and prolong the survival of patients. MicroRNAs (miRNAs) are a group of small non-protein-coding RNAs that function as repressors of diverse genes. We aimed to identi...

Comparison of machine learning techniques to predict all-cause mortality using fitness data: the Henry ford exercIse testing (FIT) project.

BMC medical informatics and decision making
BACKGROUND: Prior studies have demonstrated that cardiorespiratory fitness (CRF) is a strong marker of cardiovascular health. Machine learning (ML) can enhance the prediction of outcomes through classification techniques that classify the data into p...

Classification of optic disc shape in glaucoma using machine learning based on quantified ocular parameters.

PloS one
PURPOSE: This study aimed to develop a machine learning-based algorithm for objective classification of the optic disc in patients with open-angle glaucoma (OAG), using quantitative parameters obtained from ophthalmic examination instruments.

Artificial intelligence in fracture detection: transfer learning from deep convolutional neural networks.

Clinical radiology
AIM: To identify the extent to which transfer learning from deep convolutional neural networks (CNNs), pre-trained on non-medical images, can be used for automated fracture detection on plain radiographs.

Comparison Between Manual Auditing and a Natural Language Process With Machine Learning Algorithm to Evaluate Faculty Use of Standardized Reports in Radiology.

Journal of the American College of Radiology : JACR
PURPOSE: When implementing or monitoring department-sanctioned standardized radiology reports, feedback about individual faculty performance has been shown to be a useful driver of faculty compliance. Most commonly, these data are derived from manual...

A deep learning framework for causal shape transformation.

Neural networks : the official journal of the International Neural Network Society
Recurrent neural network (RNN) and Long Short-term Memory (LSTM) networks are the common go-to architecture for exploiting sequential information where the output is dependent on a sequence of inputs. However, in most considered problems, the depende...

A New Delay Connection for Long Short-Term Memory Networks.

International journal of neural systems
Connections play a crucial role in neural network (NN) learning because they determine how information flows in NNs. Suitable connection mechanisms may extensively enlarge the learning capability and reduce the negative effect of gradient problems. I...

Machine Learning in Detection of Undiagnosed Celiac Disease.

Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association

Symtosis: A liver ultrasound tissue characterization and risk stratification in optimized deep learning paradigm.

Computer methods and programs in biomedicine
Background and Objective Fatty Liver Disease (FLD) - a disease caused by deposition of fat in liver cells, is predecessor to terminal diseases such as liver cancer. The machine learning (ML) techniques applied for FLD detection and risk stratificatio...