AIMC Topic: Datasets as Topic

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Entity recognition in Chinese clinical text using attention-based CNN-LSTM-CRF.

BMC medical informatics and decision making
BACKGROUND: Clinical entity recognition as a fundamental task of clinical text processing has been attracted a great deal of attention during the last decade. However, most studies focus on clinical text in English rather than other languages. Recent...

Extracting health-related causality from twitter messages using natural language processing.

BMC medical informatics and decision making
BACKGROUND: Twitter messages (tweets) contain various types of topics in our daily life, which include health-related topics. Analysis of health-related tweets would help us understand health conditions and concerns encountered in our daily lives. In...

Artificial intelligence to diagnose meniscus tears on MRI.

Diagnostic and interventional imaging
PURPOSE: The purpose of this study was to build and evaluate a high-performance algorithm to detect and characterize the presence of a meniscus tear on magnetic resonance imaging examination (MRI) of the knee.

Asynchronous event-based sampling data for impulsive protocol on consensus of non-linear multi-agent systems.

Neural networks : the official journal of the International Neural Network Society
In this paper, we discuss the consensus problem of non-linear multi-agent systems where an impulsive protocol with event-based asynchronously sampled data is adopted. Systems that communicate by data asynchronously sampled in limited time intervals a...

Kidney cortex segmentation in 2D CT with U-Nets ensemble aggregation.

Diagnostic and interventional imaging
PURPOSE: This work presents our contribution to one of the data challenges organized by the French Radiology Society during the Journées Francophones de Radiologie. This challenge consisted in segmenting the kidney cortex from coronal computed tomogr...

Diagnosis of focal liver lesions from ultrasound using deep learning.

Diagnostic and interventional imaging
PURPOSE: The purpose of this study was to create an algorithm that simultaneously detects and characterizes (benign vs. malignant) focal liver lesion (FLL) using deep learning.

Detection and characterization of MRI breast lesions using deep learning.

Diagnostic and interventional imaging
PURPOSE: The purpose of this study was to assess the potential of a deep learning model to discriminate between benign and malignant breast lesions using magnetic resonance imaging (MRI) and characterize different histological subtypes of breast lesi...

Automatic knee meniscus tear detection and orientation classification with Mask-RCNN.

Diagnostic and interventional imaging
PURPOSE: This work presents our contribution to a data challenge organized by the French Radiology Society during the Journées Francophones de Radiologie in October 2018. This challenge consisted in classifying MR images of the knee with respect to t...

Evolution of Deep Convolutional Neural Networks Using Cartesian Genetic Programming.

Evolutionary computation
The convolutional neural network (CNN), one of the deep learning models, has demonstrated outstanding performance in a variety of computer vision tasks. However, as the network architectures become deeper and more complex, designing CNN architectures...