AIMC Topic: Image Processing, Computer-Assisted

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The development of an attention mechanism enhanced deep learning model and its application for body composition assessment with L3 CT images.

Scientific reports
Body composition assessment is very useful for evaluating a patient's status in the clinic, but recognizing, labeling, and calculating the body compositions would be burdensome. This study aims to develop a web-based service that could automate calcu...

Automated workflow for the cell cycle analysis of (non-)adherent cells using a machine learning approach.

eLife
Understanding the cell cycle at the single-cell level is crucial for cellular biology and cancer research. While current methods using fluorescent markers have improved the study of adherent cells, non-adherent cells remain challenging. In this study...

Classifying age from medial clavicle using a 30-year threshold: An image analysis based approach.

PloS one
This study aimed to develop image-analysis-based classification models for distinguishing individuals younger and older than 30 using the medial clavicle. We extracted 2D images of the medial clavicle from multi-slice computed tomography (MSCT) scans...

Detection of motor nervous disease using deep learning based Duple feature extraction network.

Technology and health care : official journal of the European Society for Engineering and Medicine
BackgroundA motor nervous disease (MND) is a debilitating nervous disease that affects motor neurons that regulates the muscular voluntary movements. The disease gradually destroys parts of the neurological system. Generally, MND develops owing to a ...

A Physics-Informed Deep Neural Network for Harmonization of CT Images.

IEEE transactions on bio-medical engineering
OBJECTIVE: Computed Tomography (CT) quantification is affected by the variability in image acquisition and rendition. This paper aimed to reduce this variability by harmonizing the images utilizing physics-based deep neural networks (DNNs).

SeeSaw: Learning Soft Tissue Deformation From Laparoscopy Videos With GNNs.

IEEE transactions on bio-medical engineering
A major challenge in image-guided laparoscopic surgery is that structures of interest often deform and go, even if only momentarily, out of view. Methods which rely on having an up-to-date impression of those structures, such as registration or local...

The diagnostic value of MRI segmentation technique for shoulder joint injuries based on deep learning.

Scientific reports
This work is to investigate the diagnostic value of a deep learning-based magnetic resonance imaging (MRI) image segmentation (IS) technique for shoulder joint injuries (SJIs) in swimmers. A novel multi-scale feature fusion network (MSFFN) is develop...

Enhanced MobileNet for skin cancer image classification with fused spatial channel attention mechanism.

Scientific reports
Skin Cancer, which leads to a large number of deaths annually, has been extensively considered as the most lethal tumor around the world. Accurate detection of skin cancer in its early stage can significantly raise the survival rate of patients and r...

In-context learning enables multimodal large language models to classify cancer pathology images.

Nature communications
Medical image classification requires labeled, task-specific datasets which are used to train deep learning networks de novo, or to fine-tune foundation models. However, this process is computationally and technically demanding. In language processin...

Computer-aided diagnosis of early-stage Retinopathy of Prematurity in neonatal fundus images using artificial intelligence.

Biomedical physics & engineering express
Retinopathy of Prematurity (ROP) is a retinal disorder affecting preterm babies, which can lead to permanent blindness without treatment. Early-stage ROP diagnosis is vital in providing optimal therapy for the neonates. The proposed study predicts ea...