AIMC Topic: Algorithms

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Explainable COVID-19 Detection Using Chest CT Scans and Deep Learning.

Sensors (Basel, Switzerland)
This paper explores how well deep learning models trained on chest CT images can diagnose COVID-19 infected people in a fast and automated process. To this end, we adopted advanced deep network architectures and proposed a transfer learning strategy ...

GVES: machine learning model for identification of prognostic genes with a small dataset.

Scientific reports
Machine learning may be a powerful approach to more accurate identification of genes that may serve as prognosticators of cancer outcomes using various types of omics data. However, to date, machine learning approaches have shown limited prediction a...

Analyzing Surgical Treatment of Intestinal Obstruction in Children with Artificial Intelligence.

Computational and mathematical methods in medicine
Intestinal obstruction is a common surgical emergency in children. However, it is challenging to seek appropriate treatment for childhood ileus since many diagnostic measures suitable for adults are not applicable to children. The rapid development o...

Preserving image texture while reducing radiation dose with a deep learning image reconstruction algorithm in chest CT: A phantom study.

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: To assess whether a deep learning image reconstruction algorithm (TrueFidelity) can preserve the image texture of conventional filtered back projection (FBP) at reduced dose levels attained by ASIR-V in chest CT.

Multi-Domain Adversarial Feature Generalization for Person Re-Identification.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
With the assistance of sophisticated training methods applied to single labeled datasets, the performance of fully-supervised person re-identification (Person Re-ID) has been improved significantly in recent years. However, these models trained on a ...

Reinforcement learning in surgery.

Surgery
Patients and physicians make essential decisions regarding diagnostic and therapeutic interventions. These actions should be performed or deferred under time constraints and uncertainty regarding patients' diagnoses and predicted response to treatmen...

Chest X-ray image phase features for improved diagnosis of COVID-19 using convolutional neural network.

International journal of computer assisted radiology and surgery
PURPOSE: Recently, the outbreak of the novel coronavirus disease 2019 (COVID-19) pandemic has seriously endangered human health and life. In fighting against COVID-19, effective diagnosis of infected patient is critical for preventing the spread of d...

Integration of human cell lines gene expression and chemical properties of drugs for Drug Induced Liver Injury prediction.

Biology direct
MOTIVATION: Drug-induced liver injury (DILI) is one of the primary problems in drug development. Early prediction of DILI can bring a significant reduction in the cost of clinical trials. In this work we examined whether occurrence of DILI can be pre...

Explanation and prediction of clinical data with imbalanced class distribution based on pattern discovery and disentanglement.

BMC medical informatics and decision making
BACKGROUND: Statistical data analysis, especially the advanced machine learning (ML) methods, have attracted considerable interest in clinical practices. We are looking for interpretability of the diagnostic/prognostic results that will bring confide...

An artificial neural network based mathematical model for a stochastic health care facility location problem.

Health care management science
This research is conducted to investigate the problem of locating the trauma centers and helicopters' station in order to optimize the trauma care system. The stochastic characteristics of the system, such as stochastic transferring time of the patie...