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US2Mask: Image-to-mask generation learning via a conditional GAN for cardiac ultrasound image segmentation.

Computers in biology and medicine
Cardiac ultrasound (US) image segmentation is vital for evaluating clinical indices, but it often demands a large dataset and expert annotations, resulting in high costs for deep learning algorithms. To address this, our study presents a framework ut...

Damage detection of road domain waveform guardrail structure based on machine learning multi-module fusion.

PloS one
The current highway waveform guardrail recognition technology has encountered problems with low segmentation accuracy and strong noise interference. Therefore, an improved U-net semantic segmentation model is proposed to improve the efficiency of roa...

Fully semantic segmentation for rectal cancer based on post-nCRT MRl modality and deep learning framework.

BMC cancer
PURPOSE: Rectal tumor segmentation on post neoadjuvant chemoradiotherapy (nCRT) magnetic resonance imaging (MRI) has great significance for tumor measurement, radiomics analysis, treatment planning, and operative strategy. In this study, we developed...

Latent Graph Representations for Critical View of Safety Assessment.

IEEE transactions on medical imaging
Assessing the critical view of safety in laparoscopic cholecystectomy requires accurate identification and localization of key anatomical structures, reasoning about their geometric relationships to one another, and determining the quality of their e...

Attributed Multi-Order Graph Convolutional Network for Heterogeneous Graphs.

Neural networks : the official journal of the International Neural Network Society
Heterogeneous graph neural networks play a crucial role in discovering discriminative node embeddings and relations from multi-relational networks. One of the key challenges in heterogeneous graph learning lies in designing learnable meta-paths, whic...

Deep learning for head and neck semi-supervised semantic segmentation.

Physics in medicine and biology
. Radiation therapy (RT) represents a prevalent therapeutic modality for head and neck (H&N) cancer. A crucial phase in RT planning involves the precise delineation of organs-at-risks (OARs), employing computed tomography (CT) scans. Nevertheless, th...

TFCNet: A texture-aware and fine-grained feature compensated polyp detection network.

Computers in biology and medicine
PURPOSE: Abnormal tissue detection is a prerequisite for medical image analysis and computer-aided diagnosis and treatment. The use of neural networks (CNN) to achieve accurate detection of intestinal polyps is beneficial to the early diagnosis and t...

Using deep neural networks to disentangle visual and semantic information in human perception and memory.

Nature human behaviour
Mental representations of familiar categories are composed of visual and semantic information. Disentangling the contributions of visual and semantic information in humans is challenging because they are intermixed in mental representations. Deep neu...

A systematic literature review on the applications of recurrent neural networks in code clone research.

PloS one
Code clones, referring to code fragments that are either similar or identical and are copied and pasted within software systems, have negative effects on both software quality and maintenance. The objective of this work is to systematically review an...

Explaining protein-protein interactions with knowledge graph-based semantic similarity.

Computers in biology and medicine
The application of artificial intelligence and machine learning methods for several biomedical applications, such as protein-protein interaction prediction, has gained significant traction in recent decades. However, explainability is a key aspect of...