AIMC Topic: Deep Learning

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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...

Efficient detection of foodborne pathogens via SERS and deep learning: An ADMIN-optimized NAS-Unet approach.

Journal of hazardous materials
Amid the increasing global challenge of foodborne diseases, there is an urgent need for rapid and precise pathogen detection methods. This study innovatively integrates surface-enhanced Raman Spectroscopy (SERS) with deep learning technology to devel...

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.

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 ...

[Transformation of free-text radiology reports into structured data].

Radiologie (Heidelberg, Germany)
BACKGROUND: The rapid development of large language models (LLMs) opens up new possibilities for the automated processing of medical texts. Transforming unstructured radiology reports into structured data is crucial for efficient use in clinical deci...

DeepInterAware: Deep Interaction Interface-Aware Network for Improving Antigen-Antibody Interaction Prediction from Sequence Data.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Identifying interactions between candidate antibodies and target antigens is a key step in developing effective human therapeutics. The antigen-antibody interaction (AAI) occurs at the structural level, but the limited structure data poses a signific...

Screening of estrogen receptor activity of per- and polyfluoroalkyl substances based on deep learning and in vivo assessment.

Environmental pollution (Barking, Essex : 1987)
Over the past decades, exposure to per- and polyfluoroalkyl substances (PFAS), a group of synthetic chemicals notorious for their environmental persistence, has been shown to pose increased health risks. Despite that some PFAS were reported to have e...