AIMC Topic: Image Processing, Computer-Assisted

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Frequency domain manipulation of multiple copy-move forgery in digital image forensics.

PloS one
Copy move forgery is a type of image forgery in which a portion of the original image is copied and pasted in a new location on the same image. The consistent illumination and noise pattern make this kind of forgery more difficult to detect. In copy-...

Comparison of synthetic LGE with optimal inversion time vs. conventional LGE via representation learning: Quantification of Bias in Population Analysis.

Computers in biology and medicine
PURPOSE: Late Gadolinium Enhancement (LGE) images are crucial elements of CMR protocols for evaluating myocardial infarct (MI) severity and size. However, these images rely on signal intensity changes and manual inversion time (TI) settings, leading ...

Image quality and radiation dose of reduced-dose abdominopelvic computed tomography (CT) with silver filter and deep learning reconstruction.

Scientific reports
To assess the image quality and radiation dose between reduced-dose CT with deep learning reconstruction (DLR) using SilverBeam filter and standard dose with iterative reconstruction (IR) in abdominopelvic CT. In total, 182 patients (mean age ± stand...

Multifunctional cells based neural architecture search for plant images classification.

Scientific reports
To develop a high-performance convolutional neural network (CNN) model for plant image classification automatically, we propose a neural architecture search (NAS) method tailored to multifunctional cells (MFC), termed MFC-NAS. Initially, a search spa...

Fusion of microscopic and diffraction images with VGG net for budding yeast recognition in imaging flow cytometry.

Scientific reports
Microscopic-Diffraction Imaging Flow Cytometry (MDIFC) is a high-throughput, stain-free technology that captures paired microscopic and diffraction images of cellular events, utilizing machine learning for the classification of cell subpopulations. H...

New hybrid features extracted from US images for breast cancer classification.

Scientific reports
Artificial intelligence (AI), and image processing fields play a vital role in classifying benign and malignant breast cancer (BC). The novelty of this paper lies in computing original hybrid features (HF) from textural and shape features of BC integ...

Enhancing pathological feature discrimination in diabetic retinopathy multi-classification with self-paced progressive multi-scale training.

Scientific reports
Diabetic retinopathy (DR) is a common diabetes complication that presents significant diagnostic challenges due to its reliance on expert assessment and the subtlety of small lesions. Although deep learning has shown promise, its effectiveness is oft...

Automatic segmentation of liver structures in multi-phase MRI using variants of nnU-Net and Swin UNETR.

Scientific reports
Accurate segmentation of the liver parenchyma, portal veins, hepatic veins, and lesions from MRI is important for hepatic disease monitoring and treatment. Multi-phase contrast enhanced imaging is superior in distinguishing hepatic structures compare...

ComptoNet: a Compton-map guided deep learning framework for multi-scatter estimation in multi-source stationary CT.

Physics in medicine and biology
Multi-source stationary computed tomography (MSS-CT) offers significant advantages in medical and industrial applications due to its gantryless scan architecture and capability of simultaneous multi-source emission. However, the lack of anti-scatter ...

HVUNet: A hybrid vision transformer-based UNet for accurate detection and localization in histopathology images.

Computers in biology and medicine
Precise identification of object of interest (OoI) in histopathology images plays a vital role in cancer diagnosis and prognosis. Despite advances in digital pathology, detecting specific cellular structures within these images remains a significant ...