AIMC Topic: Humans

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A Novel Multiple-View Adversarial Learning Network for Unsupervised Domain Adaptation Action Recognition.

IEEE transactions on cybernetics
Abstract-domain adaptation action recognition is a hot research topic in machine learning and some effective approaches have been proposed. However, samples in the target domain with label information are often required by these approaches. Moreover,...

Interpretability-Based Multimodal Convolutional Neural Networks for Skin Lesion Diagnosis.

IEEE transactions on cybernetics
Skin lesion diagnosis is a key step for skin cancer screening, which requires high accuracy and interpretability. Though many computer-aided methods, especially deep learning methods, have made remarkable achievements in skin lesion diagnosis, their ...

A New Consensus Model Based on Trust Interactive Weights for Intuitionistic Group Decision Making in Social Networks.

IEEE transactions on cybernetics
A promising feature for group decision making (GDM) lies in the study of the interaction between individuals. In conventional GDM research, experts are independent. This is reflected in the setting of preferences and weights. Nevertheless, each exper...

Multi-Perspective Hierarchical Deep-Fusion Learning Framework for Lung Nodule Classification.

Sensors (Basel, Switzerland)
Lung cancer is the leading cancer type that causes mortality in both men and women. Computer-aided detection (CAD) and diagnosis systems can play a very important role for helping physicians with cancer treatments. This study proposes a hierarchical ...

Evaluation of Preprocessing Methods on Independent Medical Hyperspectral Databases to Improve Analysis.

Sensors (Basel, Switzerland)
Currently, one of the most common causes of death worldwide is cancer. The development of innovative methods to support the early and accurate detection of cancers is required to increase the recovery rate of patients. Several studies have shown that...

Graph based multi-scale neighboring topology deep learning for kidney and tumor segmentation.

Physics in medicine and biology
Effective learning and modelling of spatial and semantic relations between image regions in various ranges are critical yet challenging in image segmentation tasks.We propose a novel deep graph reasoning model to learn from multi-order neighborhood t...

UnMICST: Deep learning with real augmentation for robust segmentation of highly multiplexed images of human tissues.

Communications biology
Upcoming technologies enable routine collection of highly multiplexed (20-60 channel), subcellular resolution images of mammalian tissues for research and diagnosis. Extracting single cell data from such images requires accurate image segmentation, a...

Deep multiple instance learning for predicting chemotherapy response in non-small cell lung cancer using pretreatment CT images.

Scientific reports
The individual prognosis of chemotherapy is quite different in non-small cell lung cancer (NSCLC). There is an urgent need to precisely predict and assess the treatment response. To develop a deep multiple-instance learning (DMIL) based model for pre...

Retrospective analysis and prospective validation of an AI-based software for intracranial haemorrhage detection at a high-volume trauma centre.

Scientific reports
Rapid detection of intracranial haemorrhage (ICH) is crucial for assessing patients with neurological symptoms. Prioritising these urgent scans for reporting presents a challenge for radiologists. Artificial intelligence (AI) offers a solution to ena...

Machine learning and ontology in eCoaching for personalized activity level monitoring and recommendation generation.

Scientific reports
Leading a sedentary lifestyle may cause numerous health problems. Therefore, passive lifestyle changes should be given priority to avoid severe long-term damage. Automatic health coaching system may help people manage a healthy lifestyle with continu...