AIMC Topic: Humans

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Robust breast cancer detection in mammography and digital breast tomosynthesis using an annotation-efficient deep learning approach.

Nature medicine
Breast cancer remains a global challenge, causing over 600,000 deaths in 2018 (ref. ). To achieve earlier cancer detection, health organizations worldwide recommend screening mammography, which is estimated to decrease breast cancer mortality by 20-4...

Synthetic CT image generation of shape-controlled lung cancer using semi-conditional InfoGAN and its applicability for type classification.

International journal of computer assisted radiology and surgery
PURPOSE: In recent years, convolutional neural network (CNN), an artificial intelligence technology with superior image recognition, has become increasingly popular and frequently used for classification tasks in medical imaging. However, the amount ...

Total knee arthroplasty: Latest robotics implantation techniques.

Orthopaedics & traumatology, surgery & research : OTSR
The recent introduction of new robotic systems for total knee arthroplasty (TKA) has created somewhat of a craze. Nevertheless, we can ask ourselves whether it is justified to use these new but very costly technologies. The results and limitations of...

Shallow Graph Convolutional Network for Skeleton-Based Action Recognition.

Sensors (Basel, Switzerland)
Graph convolutional networks (GCNs) have brought considerable improvement to the skeleton-based action recognition task. Existing GCN-based methods usually use the fixed spatial graph size among all the layers. It severely affects the model's abiliti...

PID++: A Computationally Lightweight Humanoid Motion Control Algorithm.

Sensors (Basel, Switzerland)
Currently robotic motion control algorithms are tedious at best to implement, are lacking in automatic situational adaptability, and tend to be static in nature. Humanoid (human-like) control is little more than a dream, for all, but the fastest comp...

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 ...

Deep learning-based computer-aided diagnosis in screening breast ultrasound to reduce false-positive diagnoses.

Scientific reports
A major limitation of screening breast ultrasound (US) is a substantial number of false-positive biopsy. This study aimed to develop a deep learning-based computer-aided diagnosis (DL-CAD)-based diagnostic model to improve the differential diagnosis ...

A novel deep learning-based quantification of serial chest computed tomography in Coronavirus Disease 2019 (COVID-19).

Scientific reports
This study aims to explore and compare a novel deep learning-based quantification with the conventional semi-quantitative computed tomography (CT) scoring for the serial chest CT scans of COVID-19. 95 patients with confirmed COVID-19 and a total of 4...

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...

Visual behavior modelling for robotic theory of mind.

Scientific reports
Behavior modeling is an essential cognitive ability that underlies many aspects of human and animal social behavior (Watson in Psychol Rev 20:158, 1913), and an ability we would like to endow robots. Most studies of machine behavior modelling, howeve...