AIMC Topic: Humans

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SGSR: style-subnets-assisted generative latent bank for large-factor super-resolution with registered medical image dataset.

International journal of computer assisted radiology and surgery
PURPOSE: We propose a large-factor super-resolution (SR) method for performing SR on registered medical image datasets. Conventional SR approaches use low-resolution (LR) and high-resolution (HR) image pairs to train a deep convolutional neural netwo...

Development of fecal microbial diagnostic marker sets of colorectal cancer using natural language processing method.

The International journal of biological markers
BACKGROUND: Cancer screening and early detection greatly increase the chances of successful treatment. However, most cancer types lack effective early screening biomarkers. In recent years, natural language processing (NLP)-based text-mining methods ...

CAD-RADS scoring of coronary CT angiography with Multi-Axis Vision Transformer: A clinically-inspired deep learning pipeline.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The standard non-invasive imaging technique used to assess the severity and extent of Coronary Artery Disease (CAD) is Coronary Computed Tomography Angiography (CCTA). However, manual grading of each patient's CCTA according...

Learning image representations for anomaly detection: Application to discovery of histological alterations in drug development.

Medical image analysis
We present a system for anomaly detection in histopathological images. In histology, normal samples are usually abundant, whereas anomalous (pathological) cases are scarce or not available. Under such settings, one-class classifiers trained on health...

Deep learning for [F]fluorodeoxyglucose-PET-CT classification in patients with lymphoma: a dual-centre retrospective analysis.

The Lancet. Digital health
BACKGROUND: The rising global cancer burden has led to an increasing demand for imaging tests such as [F]fluorodeoxyglucose ([F]FDG)-PET-CT. To aid imaging specialists in dealing with high scan volumes, we aimed to train a deep learning artificial in...

Dynamic mutual predictions during social learning: A computational and interbrain model.

Neuroscience and biobehavioral reviews
During social interactions, we constantly learn about the thoughts, feelings, and personality traits of our interaction partners. Learning in social interactions is critical for bond formation and acquiring knowledge. Importantly, this type of learni...

Pediatric evaluations for deep learning CT denoising.

Medical physics
BACKGROUND: Deep learning (DL) CT denoising models have the potential to improve image quality for lower radiation dose exams. These models are generally trained with large quantities of adult patient image data. However, CT, and increasingly DL deno...

Evaluating the Hounsfield unit assignment and dose differences between CT-based standard and deep learning-based synthetic CT images for MRI-only radiation therapy of the head and neck.

Journal of applied clinical medical physics
BACKGROUND: Magnetic resonance image only (MRI-only) simulation for head and neck (H&N) radiotherapy (RT) could allow for single-image modality planning with excellent soft tissue contrast. In the MRI-only simulation workflow, synthetic computed tomo...

Assessment of Quality Outcomes and the Learning Curve for Robot-Assisted Anatomical Lung Resections.

Journal of laparoendoscopic & advanced surgical techniques. Part A
To determine the perioperative quality assessment results and learning curves for robot-assisted anatomical lung resection. We analyzed the data of the initial 400 patients who underwent lobectomies or segmentectomies by 1 surgeon from January 2020...

An attention-based deep learning method for right ventricular quantification using 2D echocardiography: Feasibility and accuracy.

Echocardiography (Mount Kisco, N.Y.)
AIM: To test the feasibility and accuracy of a new attention-based deep learning (DL) method for right ventricular (RV) quantification using 2D echocardiography (2DE) with cardiac magnetic resonance imaging (CMR) as reference.