AIMC Topic: Algorithms

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A Generic Human-Machine Annotation Framework Based on Dynamic Cooperative Learning.

IEEE transactions on cybernetics
The task of obtaining meaningful annotations is a tedious work, incurring considerable costs and time consumption. Dynamic active learning and cooperative learning are recently proposed approaches to reduce human effort of annotating data with subjec...

Incorporating prior knowledge via volumetric deep residual network to optimize the reconstruction of sparsely sampled MRI.

Magnetic resonance imaging
For sparse sampling that accelerates magnetic resonance (MR) image acquisition, non-linear reconstruction algorithms have been developed, which incorporated patient specific a prior information. More generic a prior information could be acquired via ...

Effects of Subsystem and Coupling on Synchronization of Multiple Neural Networks With Delays via Impulsive Coupling.

IEEE transactions on neural networks and learning systems
This paper from new perspectives discusses the global synchronization of multiple recurrent neural networks (MNNs) with time delays via impulsive coupling. A new concept (coupling strength) is introduced, it is a variable parameter and plays a key ro...

Joint sparse graph and flexible embedding for graph-based semi-supervised learning.

Neural networks : the official journal of the International Neural Network Society
This letter introduces a framework for graph-based semi-supervised learning by estimating a flexible non-linear projection and its linear regression model. Unlike existing works, the proposed framework jointly estimates the graph structure, the non-l...

Medical ethics considerations on artificial intelligence.

Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
Artificial intelligence (AI) is currently one of the mostly controversial matters of the world. This article discusses AI in terms of the medical ethics issues involved, both existing and potential. Once artificial intelligence is fully developed wit...

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...

netDx: interpretable patient classification using integrated patient similarity networks.

Molecular systems biology
Patient classification has widespread biomedical and clinical applications, including diagnosis, prognosis, and treatment response prediction. A clinically useful prediction algorithm should be accurate, generalizable, be able to integrate diverse da...

Automatically identifying social isolation from clinical narratives for patients with prostate Cancer.

BMC medical informatics and decision making
BACKGROUND: Social isolation is an important social determinant that impacts health outcomes and mortality among patients. The National Academy of Medicine recently recommended that social isolation be documented in electronic health records (EHR). H...

TADKB: Family classification and a knowledge base of topologically associating domains.

BMC genomics
BACKGROUND: Topologically associating domains (TADs) are considered the structural and functional units of the genome. However, there is a lack of an integrated resource for TADs in the literature where researchers can obtain family classifications a...

Deep Collaborative Learning With Application to the Study of Multimodal Brain Development.

IEEE transactions on bio-medical engineering
OBJECTIVE: Multi-modal functional magnetic resonance imaging has been widely used for brain research. Conventional data-fusion methods cannot capture complex relationship (e.g., nonlinear predictive relationship) between multiple data. This paper aim...