AIMC Topic: Deep Learning

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Unassisted Clinicians Versus Deep Learning-Assisted Clinicians in Image-Based Cancer Diagnostics: Systematic Review With Meta-analysis.

Journal of medical Internet research
BACKGROUND: A number of publications have demonstrated that deep learning (DL) algorithms matched or outperformed clinicians in image-based cancer diagnostics, but these algorithms are frequently considered as opponents rather than partners. Despite ...

A deep-learning algorithm to classify skin lesions from mpox virus infection.

Nature medicine
Undetected infection and delayed isolation of infected individuals are key factors driving the monkeypox virus (now termed mpox virus or MPXV) outbreak. To enable earlier detection of MPXV infection, we developed an image-based deep convolutional neu...

Learn2Reg: Comprehensive Multi-Task Medical Image Registration Challenge, Dataset and Evaluation in the Era of Deep Learning.

IEEE transactions on medical imaging
Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medical image registration approaches on a wide range of clinically releva...

Deep Learning-Based Image Registration in Dynamic Myocardial Perfusion CT Imaging.

IEEE transactions on medical imaging
Registration of dynamic CT image sequences is a crucial preprocessing step for clinical evaluation of multiple physiological determinants in the heart such as global and regional myocardial perfusion. In this work, we present a deformable deep learni...

Automatic reorientation by deep learning to generate short-axis SPECT myocardial perfusion images.

Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
BACKGROUND: Single photon emission computed tomography (SPECT) myocardial perfusion images (MPI) can be displayed both in traditional short-axis (SA) cardiac planes and polar maps for interpretation and quantification. It is essential to reorient the...

Predicting survival after radiosurgery in patients with lung cancer brain metastases using deep learning of radiomics and EGFR status.

Physical and engineering sciences in medicine
The early prediction of overall survival (OS) in patients with lung cancer brain metastases (BMs) after Gamma Knife radiosurgery (GKRS) can facilitate patient management and outcome improvement. However, the disease progression is influenced by multi...

A review of deep learning-based multiple-lesion recognition from medical images: classification, detection and segmentation.

Computers in biology and medicine
Deep learning-based methods have become the dominant methodology in medical image processing with the advancement of deep learning in natural image classification, detection, and segmentation. Deep learning-based approaches have proven to be quite ef...

Deep learning based MRI reconstruction with transformer.

Computer methods and programs in biomedicine
Magnetic resonance imaging (MRI) has become one of the most powerful imaging techniques in medical diagnosis, yet the prolonged scanning time becomes a bottleneck for application. Reconstruction methods based on compress sensing (CS) have made progre...

Radiomics approach with deep learning for predicting T4 obstructive colorectal cancer using CT image.

Abdominal radiology (New York)
OBJECTIVES: Patients with T4 obstructive colorectal cancer (OCC) have a high mortality rate. Therefore, an accurate distinction between T4 and T1-T3 (NT4) in OCC is an important part of preoperative evaluation, especially in the emergency setting. Th...

Application of Deep Learning System Technology in Identification of Women's Breast Cancer.

Medicina (Kaunas, Lithuania)
: The classification of breast cancer is performed based on its histological subtypes using the degree of differentiation. However, there have been low levels of intra- and inter-observer agreement in the process. The use of convolutional neural netw...