AIMC Topic: Algorithms

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TriCvT-DTI: Predicting Drug-Target Interactions Using Trimodal Representations and Convolutional Vision Transformers.

IEEE journal of biomedical and health informatics
Predicting interactions between drugs and their targets is vital for drug discovery and repositioning. Conventional techniques are slow and labor-intensive, while deep learning algorithms offer efficient solutions. However, deep learning often focus ...

Boundary-Enhanced $U^{2}$-Net for Simultaneous Four-Chamber Segmentation in Transthoracic Echocardiography.

IEEE journal of biomedical and health informatics
The heart, responsible for circulating blood throughout our body, contains four chambers. Existing analysis methods primarily focus on one single ventricle. Transthoracic echocardiography provides real-time estimations of cardiac function and enables...

Coarse for Fine: Bounding Box Supervised Thyroid Ultrasound Image Segmentation Using Spatial Arrangement and Hierarchical Prediction Consistency.

IEEE journal of biomedical and health informatics
Weakly-supervised learning methods have become increasingly attractive for medical image segmentation, but suffered from a high dependence on quantifying the pixel-wise affinities of low-level features, which are easily corrupted in thyroid ultrasoun...

Enhancing Medical Vision-Language Contrastive Learning via Inter-Matching Relation Modeling.

IEEE transactions on medical imaging
Medical image representations can be learned through medical vision-language contrastive learning (mVLCL) where medical imaging reports are used as weak supervision through image-text alignment. These learned image representations can be transferred ...

Cardiac Phase Estimation Using Deep Learning Analysis of Pulsed-Mode Projections: Toward Autonomous Cardiac CT Imaging.

IEEE transactions on medical imaging
Cardiac CT plays an important role in diagnosing heart diseases but is conventionally limited by its complex workflow that requires dedicated phase and bolus tracking devices [e.g., electrocardiogram (ECG) gating]. This work reports first progress to...

Rethinking Appearance-Based Deep Gait Recognition: Reviews, Analysis, and Insights From Gait Recognition Evolution.

IEEE transactions on neural networks and learning systems
Gait recognition is a prominent biometric recognition technique extensively employed in public security. Appearance-based and model-based gait recognition are two categories of methods commonly used. Specifically, appearance-based methods, which use ...

scDMSC: Deep Multi-View Subspace Clustering for Single-Cell Multi-Omics Data.

IEEE journal of biomedical and health informatics
Single-cell multi-omics sequencing technology comprehensively considers various molecular features to reveal the complexity of cells information. The clustering analysis of multi-omics data provides new insight into cellular heterogeneity. However, m...

Score-Based Diffusion Models With Self-Supervised Learning for Accelerated 3D Multi-Contrast Cardiac MR Imaging.

IEEE transactions on medical imaging
Long scan time significantly hinders the widespread applications of three-dimensional multi-contrast cardiac magnetic resonance (3D-MC-CMR) imaging. This study aims to accelerate 3D-MC-CMR acquisition by a novel method based on score-based diffusion ...

STSF: Spiking Time Sparse Feedback Learning for Spiking Neural Networks.

IEEE transactions on neural networks and learning systems
Spiking neural networks (SNNs) are biologically plausible models known for their computational efficiency. A significant advantage of SNNs lies in the binary information transmission through spike trains, eliminating the need for multiplication opera...

AGCLNDA: Enhancing the Prediction of ncRNA-Drug Resistance Association Using Adaptive Graph Contrastive Learning.

IEEE journal of biomedical and health informatics
Non-coding RNAs (ncRNAs), which do not encode proteins, have been implicated in chemotherapy resistance in cancer treatment. Given the high costs and time requirements of traditional biological experiments, there is an increasing need for computation...