AIMC Topic: Pattern Recognition, Automated

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A data-driven approach to referable diabetic retinopathy detection.

Artificial intelligence in medicine
UNLABELLED: Prior art on automated screening of diabetic retinopathy and direct referral decision shows promising performance; yet most methods build upon complex hand-crafted features whose performance often fails to generalize.

Counteracting Electrode Shifts in Upper-Limb Prosthesis Control via Transfer Learning.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Research on machine learning approaches for upper-limb prosthesis control has shown impressive progress. However, translating these results from the lab to patient's everyday lives remains a challenge because advanced control schemes tend to break do...

Minimalistic Approach to Coreference Resolution in Lithuanian Medical Records.

Computational and mathematical methods in medicine
Coreference resolution is a challenging part of natural language processing (NLP) with applications in machine translation, semantic search and other information retrieval, and decision support systems. Coreference resolution requires linguistic prep...

Semisupervised Deep Stacking Network with Adaptive Learning Rate Strategy for Motor Imagery EEG Recognition.

Neural computation
Practical motor imagery electroencephalogram (EEG) data-based applications are limited by the waste of unlabeled samples in supervised learning and excessive time consumption in the pretraining period. A semisupervised deep stacking network with an a...

Brain Morphometry Methods for Feature Extraction in Random Subspace Ensemble Neural Network Classification of First-Episode Schizophrenia.

Neural computation
Machine learning (ML) is a growing field that provides tools for automatic pattern recognition. The neuroimaging community currently tries to take advantage of ML in order to develop an auxiliary diagnostic tool for schizophrenia diagnostics. In this...

A dense multi-path decoder for tissue segmentation in histopathology images.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Segmenting different tissue components in histopathological images is of great importance for analyzing tissues and tumor environments. In recent years, an encoder-decoder family of convolutional neural networks has increasi...

Analysis and evaluation of handwriting in patients with Parkinson's disease using kinematic, geometrical, and non-linear features.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Parkinson's disease is a neurological disorder that affects the motor system producing lack of coordination, resting tremor, and rigidity. Impairments in handwriting are among the main symptoms of the disease. Handwriting a...

Convolutional neural network for cell classification using microscope images of intracellular actin networks.

PloS one
Automated cell classification is an important yet a challenging computer vision task with significant benefits to biomedicine. In recent years, there have been several studies attempted to build an artificial intelligence-based cell classifier using ...

Feature Aggregation With Reinforcement Learning for Video-Based Person Re-Identification.

IEEE transactions on neural networks and learning systems
Video-based person re-identification (re-id) matches two tracks of persons from different cameras. Features are extracted from the images of a sequence and then aggregated as a track feature. Compared to existing works that aggregate frame features b...

Discriminative multi-source adaptation multi-feature co-regression for visual classification.

Neural networks : the official journal of the International Neural Network Society
Learning an effective visual classifier from few labeled samples is a challenging problem, which has motivated the multi-source adaptation scheme in machine learning. While the advantages of multi-source adaptation have been widely recognized, there ...