AIMC Topic: Humans

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Diffusion-driven multi-modality medical image fusion.

Medical & biological engineering & computing
Multi-modality medical image fusion (MMIF) technology utilizes the complementarity of different modalities to provide more comprehensive diagnostic insights for clinical practice. Existing deep learning-based methods often focus on extracting the pri...

Hierarchical task network-enhanced multi-agent reinforcement learning: Toward efficient cooperative strategies.

Neural networks : the official journal of the International Neural Network Society
Navigating multi-agent reinforcement learning (MARL) environments with sparse rewards is notoriously difficult, particularly in suboptimal settings where exploration can be prematurely halted. To tackle these challenges, we introduce Hierarchical Sym...

Span-aware pre-trained network with deep information bottleneck for scientific entity relation extraction.

Neural networks : the official journal of the International Neural Network Society
Scientific entity relation extraction intends to promote the performance of each subtask through exploring the contextual representations with rich scientific semantics. However, most of existing models encounter the dilemma of scientific semantic di...

Eliminating the second CT scan of dual-tracer total-body PET/CT via deep learning-based image synthesis and registration.

European journal of nuclear medicine and molecular imaging
PURPOSE: This study aims to develop and validate a deep learning framework designed to eliminate the second CT scan of dual-tracer total-body PET/CT imaging.

Using Natural Language Processing Methods to Predict Topics Included in 2019 Ohio Syphilis Disease Intervention Specialist Records.

Sexually transmitted diseases
BACKGROUND: Free-text notes in disease intervention specialist (DIS) records may contain relevant information for sexual transmitted infection control. In their current form, the notes are not analyzable without manual reading, which is labor-intensi...

EMBANet: A flexible efficient multi-branch attention network.

Neural networks : the official journal of the International Neural Network Society
Recent advances in the design of convolutional neural networks have shown that performance can be enhanced by improving the ability to represent multi-scale features. However, most existing methods either focus on designing more sophisticated attenti...

Cross prior Bayesian attention with correlated inception and residual learning for brain tumor classification using MR images (CB-CIRL Net).

Journal of neuroscience methods
BACKGROUND: Brain tumor classification from magnetic resonance (MR) images is crucial for early diagnosis and effective treatment planning. However, the homogeneity of tumors across different categories poses a challenge. Although, attention-based co...

A deep learning-based peer review method for radiotherapy planning.

Medical physics
BACKGROUND: Quality control (QC) in radiotherapy planning is crucial for ensuring treatment efficacy and patient safety. Traditionally, QC relies on standard indicators and subjective assessments, which may lead to inconsistencies.

Double-mix pseudo-label framework: enhancing semi-supervised segmentation on category-imbalanced CT volumes.

International journal of computer assisted radiology and surgery
PURPOSE: Deep-learning-based supervised CT segmentation relies on fully and densely labeled data, the labeling process of which is time-consuming. In this study, our proposed method aims to improve segmentation performance on CT volumes with limited ...

A novel neural network-based nearest neighbor approach for drug function prediction from chemical structures.

European journal of pharmacology
Drug function prediction is a crucial task in drug discovery, design, and development, which involves the prediction of the biological functions of a drug molecule based on its chemical structure. Misleading drug function is a common reason for adver...