AIMC Topic: Image Processing, Computer-Assisted

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Multistage deep learning methods for automating radiographic sharp score prediction in rheumatoid arthritis.

Scientific reports
The Sharp-van der Heijde score (SvH) is crucial for assessing joint damage in rheumatoid arthritis (RA) through radiographic images. However, manual scoring is time-consuming and subject to variability. This study proposes a multistage deep learning ...

Multi scale multi attention network for blood vessel segmentation in fundus images.

Scientific reports
Precise segmentation of retinal vasculature is crucial for the early detection, diagnosis, and treatment of vision-threatening ailments. However, this task is challenging due to limited contextual information, variations in vessel thicknesses, the co...

STAIG: Spatial transcriptomics analysis via image-aided graph contrastive learning for domain exploration and alignment-free integration.

Nature communications
Spatial transcriptomics is an essential application for investigating cellular structures and interactions and requires multimodal information to precisely study spatial domains. Here, we propose STAIG, a deep-learning model that integrates gene expr...

Continuous single-ended depth-of-interaction measurement using highly multiplexed signals and artificial neural networks.

Physics in medicine and biology
. This study aims to enhance positron emission tomography (PET) imaging systems by developing a continuous depth-of-interaction (DOI) measurement technique using a single-ended readout. Our primary focus is on reducing the number of readout channels ...

Enhanced ResNet-50 for garbage classification: Feature fusion and depth-separable convolutions.

PloS one
As people's material living standards continue to improve, the types and quantities of household garbage they generate rapidly increase. Therefore, it is urgent to develop a reasonable and effective method for garbage classification. This is importan...

Classification of CT scan and X-ray dataset based on deep learning and particle swarm optimization.

PloS one
In 2019, the novel coronavirus swept the world, exposing the monitoring and early warning problems of the medical system. Computer-aided diagnosis models based on deep learning have good universality and can well alleviate these problems. However, tr...

Regional Image Quality Scoring for 2-D Echocardiography Using Deep Learning.

Ultrasound in medicine & biology
OBJECTIVE: To develop and compare methods to automatically estimate regional ultrasound image quality for echocardiography separate from view correctness.

A deep architecture based on attention mechanisms for effective end-to-end detection of early and mature malaria parasites in a realistic scenario.

Computers in biology and medicine
BACKGROUND: Malaria is a critical and potentially fatal disease caused by the Plasmodium parasite and is responsible for more than 600,000 deaths globally. Early and accurate detection of malaria parasites is crucial for effective treatment, yet conv...

Retinal OCT image classification based on MGR-GAN.

Medical & biological engineering & computing
Accurately classifying optical coherence tomography (OCT) images is essential for diagnosing and treating ophthalmic diseases. This paper introduces a novel generative adversarial network framework called MGR-GAN. The masked image modeling (MIM) meth...

Unrolled deep learning for breast cancer detection using limited-view photoacoustic tomography data.

Medical & biological engineering & computing
Photoacoustic tomography (PAT) has emerged as a promising imaging modality for breast cancer detection, offering unique advantages in visualizing tissue composition without ionizing radiation. However, limited-view scenarios in clinical settings pres...