AIMC Topic: Humans

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Identification of intraductal carcinoma of the prostate on tissue specimens using Raman micro-spectroscopy: A diagnostic accuracy case-control study with multicohort validation.

PLoS medicine
BACKGROUND: Prostate cancer (PC) is the most frequently diagnosed cancer in North American men. Pathologists are in critical need of accurate biomarkers to characterize PC, particularly to confirm the presence of intraductal carcinoma of the prostate...

Machine Learning based histology phenotyping to investigate the epidemiologic and genetic basis of adipocyte morphology and cardiometabolic traits.

PLoS computational biology
Genetic studies have recently highlighted the importance of fat distribution, as well as overall adiposity, in the pathogenesis of obesity-associated diseases. Using a large study (n = 1,288) from 4 independent cohorts, we aimed to investigate the re...

Gaussian process inference modelling of dynamic robot control for expressive piano playing.

PloS one
Piano is a complex instrument, which humans learn to play after many years of practice. This paper investigates the complex dynamics of the embodied interactions between a human and piano, in order to gain insights into the nature of humans' physical...

Bone metastasis classification using whole body images from prostate cancer patients based on convolutional neural networks application.

PloS one
Bone metastasis is one of the most frequent diseases in prostate cancer; scintigraphy imaging is particularly important for the clinical diagnosis of bone metastasis. Up to date, minimal research has been conducted regarding the application of machin...

Automated diatom searching in the digital scanning electron microscopy images of drowning cases using the deep neural networks.

International journal of legal medicine
Forensic diatom test has been widely accepted as a way of providing supportive evidences in the diagnosis of drowning. The current workflow is primarily based on the observation of diatoms by forensic pathologists under a microscopy, and this process...

The deep learning model combining CT image and clinicopathological information for predicting ALK fusion status and response to ALK-TKI therapy in non-small cell lung cancer patients.

European journal of nuclear medicine and molecular imaging
PURPOSE: This study aimed to investigate the deep learning model (DLM) combining computed tomography (CT) images and clinicopathological information for predicting anaplastic lymphoma kinase (ALK) fusion status in non-small cell lung cancer (NSCLC) p...

A preliminary study of deep learning-based reconstruction specialized for denoising in high-frequency domain: usefulness in high-resolution three-dimensional magnetic resonance cisternography of the cerebellopontine angle.

Neuroradiology
PURPOSE: Deep learning-based reconstruction (DLR) has been developed to reduce image noise and increase the signal-to-noise ratio (SNR). We aimed to evaluate the efficacy of DLR for high spatial resolution (HR)-MR cisternography.

Thyroid Ultrasound Reports: Will the Thyroid Imaging, Reporting, and Data System Improve Natural Language Processing Capture of Critical Thyroid Nodule Features?

The Journal of surgical research
BACKGROUND: Critical thyroid nodule features are contained in unstructured ultrasound (US) reports. The Thyroid Imaging, Reporting, and Data System (TI-RADS) uses five key features to risk stratify nodules and recommend appropriate intervention. This...

Keep it real: rethinking the primacy of experimental control in cognitive neuroscience.

NeuroImage
Naturalistic experimental paradigms in neuroimaging arose from a pressure to test the validity of models we derive from highly-controlled experiments in real-world contexts. In many cases, however, such efforts led to the realization that models deve...