AIMC Topic: Algorithms

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GO-MAE: Self-supervised pre-training via masked autoencoder for OCT image classification of gynecology.

Neural networks : the official journal of the International Neural Network Society
Genitourinary syndrome of menopause (GSM) is a physiological disorder caused by reduced levels of oestrogen in menopausal women. Gradually, its symptoms worsen with age and prolonged menopausal status, which gravely impacts the quality of life as wel...

Optimized deep learning networks for accurate identification of cancer cells in bone marrow.

Neural networks : the official journal of the International Neural Network Society
Radiologists utilize pictures from X-rays, magnetic resonance imaging, or computed tomography scans to diagnose bone cancer. Manual methods are labor-intensive and may need specialized knowledge. As a result, creating an automated process for disting...

Language-based reasoning graph neural network for commonsense question answering.

Neural networks : the official journal of the International Neural Network Society
Language model (LM) has played an increasingly important role in the common-sense understanding and reasoning in the CSQA task (Common Sense Question Answering). However, due to the amount of model parameters, increasing training data helps little in...

Uncertainty guided semi-supervised few-shot segmentation with prototype level fusion.

Neural networks : the official journal of the International Neural Network Society
Few-Shot Semantic Segmentation (FSS) aims to tackle the challenge of segmenting novel categories with limited annotated data. However, given the diversity among support-query pairs, transferring meta-knowledge to unseen categories poses a significant...

GMNI: Achieve good data augmentation in unsupervised graph contrastive learning.

Neural networks : the official journal of the International Neural Network Society
Graph contrastive learning (GCL) shows excellent potential in unsupervised graph representation learning. Data augmentation (DA), responsible for generating diverse views, plays a vital role in GCL, and its optimal choice heavily depends on the downs...

Deployable mixed-precision quantization with co-learning and one-time search.

Neural networks : the official journal of the International Neural Network Society
Mixed-precision quantization plays a pivotal role in deploying deep neural networks in resource-constrained environments. However, the task of finding the optimal bit-width configurations for different layers under deployable mixed-precision quantiza...

Dynamic meta-graph convolutional recurrent network for heterogeneous spatiotemporal graph forecasting.

Neural networks : the official journal of the International Neural Network Society
Spatiotemporal Graph (STG) forecasting is an essential task within the realm of spatiotemporal data mining and urban computing. Over the past few years, Spatiotemporal Graph Neural Networks (STGNNs) have gained significant attention as promising solu...

BiLSTM-Filt: Neural network for radar word segmentation.

Neural networks : the official journal of the International Neural Network Society
Radar word extraction is the analysis foundation for multi-function radars (MFRs) in electronic intelligence (ELINT). Although neural networks enhance performance in radar word extraction, current research still faces challenges from complex electrom...

Enhancing indoor PM predictions based on land use and indoor environmental factors by applying machine learning and spatial modeling approaches.

Environmental pollution (Barking, Essex : 1987)
The presence of fine particulate matter (PM) indoors constitutes a significant component of overall PM exposure, as individuals spend 90% of their time indoors; however, personal monitoring for large cohorts is often impractical. In light of this, th...

Multi-scale dual-channel feature embedding decoder for biomedical image segmentation.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Attaining global context along with local dependencies is of paramount importance for achieving highly accurate segmentation of objects from image frames and is challenging while developing deep learning-based biomedical ima...