AIMC Topic: Humans

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Robust asynchronous control of ERP-Based brain-Computer interfaces using deep learning.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Brain-computer interfaces (BCI) based on event-related potentials (ERP) are a promising technology for alternative and augmented communication in an assistive context. However, most approaches to date are synchronous, requir...

[The first data challenge of the french society of pathology: An international competition in 2020, a research tool in A.I. for the future?].

Annales de pathologie
The french society of pathology (SFP) organized in 2020 its first data challenge with the help of Health Data Hub (HDH). The organisation of this event first consisted in recruiting almost 5000 slides of uterus cervical biopsies obtained in 20 pathol...

Classifying social determinants of health from unstructured electronic health records using deep learning-based natural language processing.

Journal of biomedical informatics
OBJECTIVE: Social determinants of health (SDOH) are non-medical factors that can profoundly impact patient health outcomes. However, SDOH are rarely available in structured electronic health record (EHR) data such as diagnosis codes, and more commonl...

Deep CNN with Hybrid Binary Local Search and Particle Swarm Optimizer for Exudates Classification from Fundus Images.

Journal of digital imaging
Diabetic retinopathy is a chronic condition that causes vision loss if not detected early. In the early stage, it can be diagnosed with the aid of exudates which are called lesions. However, it is arduous to detect the exudate lesion due to the avail...

Predicting SARS-CoV-2 infection duration at hospital admission:a deep learning solution.

Medical & biological engineering & computing
COVID-19 cases are increasing around the globe with almost 5 million of deaths. We propose here a deep learning model capable of predicting the duration of the infection by means of information available at hospital admission. A total of 222 patients...

Coherence Constrained Graph LSTM for Group Activity Recognition.

IEEE transactions on pattern analysis and machine intelligence
This work aims to address the group activity recognition problem by exploring human motion characteristics. Traditional methods hold that the motions of all persons contribute equally to the group activity, which suppresses the contributions of some ...

Learning to Compose and Reason with Language Tree Structures for Visual Grounding.

IEEE transactions on pattern analysis and machine intelligence
Grounding natural language in images, such as localizing "the black dog on the left of the tree", is one of the core problems in artificial intelligence, as it needs to comprehend the fine-grained language compositions. However, existing solutions me...