AIMC Topic: Humans

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A primer on applying AI synergistically with domain expertise to oncology.

Biochimica et biophysica acta. Reviews on cancer
BACKGROUND: The concurrent growth of large-scale oncology data alongside the computational methods with which to analyze and model it has created a promising environment for revolutionizing cancer diagnosis, treatment, prevention, and drug discovery....

Epistemic Autonomy: Self-supervised Learning in the Mammalian Hippocampus.

Trends in cognitive sciences
Biological cognition is based on the ability to autonomously acquire knowledge, or epistemic autonomy. Such self-supervision is largely absent in artificial neural networks (ANN) because they depend on externally set learning criteria. Yet training A...

Liver tumor segmentation using 2.5D UV-Net with multi-scale convolution.

Computers in biology and medicine
Liver tumor segmentation networks are generally based on U-shaped encoder-decoder network with 2D or 3D structure. However, 2D networks lose the inter-layer information of continuous slices and 3D networks might introduce unacceptable parameters for ...

Intra-domain task-adaptive transfer learning to determine acute ischemic stroke onset time.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Treatment of acute ischemic strokes (AIS) is largely contingent upon the time since stroke onset (TSS). However, TSS may not be readily available in up to 25% of patients with unwitnessed AIS. Current clinical guidelines for patients with unknown TSS...

Evaluating renal lesions using deep-learning based extension of dual-energy FoV in dual-source CT-A retrospective pilot study.

European journal of radiology
PURPOSE: Dual-source (DS) CT, dual-energy (DE) field of view (FoV) is limited to the size of the smaller detector array. The purpose was to establish a deep learning-based approach to DE extrapolation by estimating missing image data using data from ...

Comparison of image quality and lesion diagnosis in abdominopelvic unenhanced CT between reduced-dose CT using deep learning post-processing and standard-dose CT using iterative reconstruction: A prospective study.

European journal of radiology
PURPOSE: To compare image quality and lesion diagnosis between reduced-dose abdominopelvic unenhanced computed tomography (CT) using deep learning (DL) post-processing and standard-dose CT using iterative reconstruction (IR).

Development Issues of Healthcare Robots: Compassionate Communication for Older Adults with Dementia.

International journal of environmental research and public health
Although progress is being made in affective computing, issues remain in enabling the effective expression of compassionate communication by healthcare robots. Identifying, describing and reconciling these concerns are important in order to provide q...

Diagnosis of Leukaemia in Blood Slides Based on a Fine-Tuned and Highly Generalisable Deep Learning Model.

Sensors (Basel, Switzerland)
Leukaemia is a dysfunction that affects the production of white blood cells in the bone marrow. Young cells are abnormally produced, replacing normal blood cells. Consequently, the person suffers problems in transporting oxygen and in fighting infect...

Application of Supervised SOM Algorithms in Predicting the Hepatotoxic Potential of Drugs.

International journal of molecular sciences
The hepatotoxic potential of drugs is one of the main reasons why a number of drugs never reach the market or have to be withdrawn from the market. Therefore, the evaluation of the hepatotoxic potential of drugs is an important part of the drug devel...

Machine learning to reveal an astute risk predictive framework for Gynecologic Cancer and its impact on women psychology: Bangladeshi perspective.

BMC bioinformatics
BACKGROUND: In this research, an astute system has been developed by using machine learning and data mining approach to predict the risk level of cervical and ovarian cancer in association to stress.