AIMC Topic: Humans

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Research on intelligent analysis strategies to improve athletes' psychological experience in the era of artificial intelligence.

Progress in neuro-psychopharmacology & biological psychiatry
In order to improve the psychological quality of athletes, this paper combines artificial intelligence technology to quantitatively analyze the psychological experience of athletes. Moreover, in view of the insufficiency of the ASI method to standard...

FWLICM-Deep Learning: Fuzzy Weighted Local Information C-Means Clustering-Based Lung Lobe Segmentation with Deep Learning for COVID-19 Detection.

Journal of digital imaging
Coronavirus (COVID-19) creates an extensive range of respiratory contagions, and it is a kind of ribonucleic acid (RNA) virus, which affects both animals and humans. Moreover, COVID-19 is a new disease, which produces contamination in upper respirati...

Overcoming the impact of physiologic tremors in ophthalmology.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
PURPOSE: Ophthalmic surgery involves the manipulation of micron-level sized structures such as the internal limiting membrane where tactile sensation is practically absent. All humans have physiologic tremors that are of low amplitude and not discern...

Deep Learning-driven classification of external DICOM studies for PACS archiving.

European radiology
OBJECTIVES: Over the course of their treatment, patients often switch hospitals, requiring staff at the new hospital to import external imaging studies to their local database. In this study, the authors present MOdality Mapping and Orchestration (MO...

Automatic segmentation of thoracic CT images using three deep learning models.

Cancer radiotherapie : journal de la Societe francaise de radiotherapie oncologique
PURPOSE: Deep learning (DL) techniques are widely used in medical imaging and in particular for segmentation. Indeed, manual segmentation of organs at risk (OARs) is time-consuming and suffers from inter- and intra-observer segmentation variability. ...

Solid Attenuation Components Attention Deep Learning Model to Predict Micropapillary and Solid Patterns in Lung Adenocarcinomas on Computed Tomography.

Annals of surgical oncology
BACKGROUND: High-grade adenocarcinoma subtypes (micropapillary and solid) treated with sublobar resection have an unfavorable prognosis compared with those treated with lobectomy. We investigated the potential of incorporating solid attenuation compo...

Histopathologic Basis for a Chest CT Deep Learning Survival Prediction Model in Patients with Lung Adenocarcinoma.

Radiology
Background A preoperative CT-based deep learning (DL) prediction model was proposed to estimate disease-free survival in patients with resected lung adenocarcinoma. However, the black-box nature of DL hinders interpretation of its results. Purpose To...

Conceptual alignment in a joint picture-naming task performed with a social robot.

Cognition
In this study we investigated whether people conceptually align when performing a language task together with a robot. In a joint picture-naming task, 24 French native speakers took turns with a robot in naming images of objects belonging to fifteen ...