AIMC Topic: Image Processing, Computer-Assisted

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A performance comparison of convolutional neural network-based image denoising methods: The effect of loss functions on low-dose CT images.

Medical physics
PURPOSE: Convolutional neural network (CNN)-based image denoising techniques have shown promising results in low-dose CT denoising. However, CNN often introduces blurring in denoised images when trained with a widely used pixel-level loss function. P...

ADN: Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction.

IEEE transactions on medical imaging
Current deep neural network based approaches to computed tomography (CT) metal artifact reduction (MAR) are supervised methods that rely on synthesized metal artifacts for training. However, as synthesized data may not accurately simulate the underly...

Bimodal learning via trilogy of skip-connection deep networks for diabetic retinopathy risk progression identification.

International journal of medical informatics
BACKGROUND: Diabetic Retinopathy (DR) is considered a pathology of retinal vascular complications, which stays in the top causes of vision impairment and blindness. Therefore, precisely inspecting its progression enables the ophthalmologists to set u...

Learning from adversarial medical images for X-ray breast mass segmentation.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Simulation of diverse lesions in images is proposed and applied to overcome the scarcity of labeled data, which has hindered the application of deep learning in medical imaging. However, most of current studies focus on gene...

Prediction of high proliferative index in pituitary macroadenomas using MRI-based radiomics and machine learning.

Neuroradiology
PURPOSE: Pituitary adenomas are among the most frequent intracranial tumors. They may exhibit clinically aggressive behavior, with recurrent disease and resistance to multimodal therapy. The ki-67 labeling index represents a proliferative marker whic...

Deep learning-based color holographic microscopy.

Journal of biophotonics
We report a framework based on a generative adversarial network that performs high-fidelity color image reconstruction using a single hologram of a sample that is illuminated simultaneously by light at three different wavelengths. The trained network...

TGV Upsampling: A Making-Up Operation for Semantic Segmentation.

Computational intelligence and neuroscience
With the widespread use of deep learning methods, semantic segmentation has achieved great improvements in recent years. However, many researchers have pointed out that with multiple uses of convolution and pooling operations, great information loss ...

Medical Image Segmentation Algorithm Based on Feedback Mechanism CNN.

Contrast media & molecular imaging
With the development of computer vision and image segmentation technology, medical image segmentation and recognition technology has become an important part of computer-aided diagnosis. The traditional image segmentation method relies on artificial ...

Characterization of Nanoscale Organization of F-Actin in Morphologically Distinct Dendritic Spines Using Supervised Learning.

eNeuro
The cytoarchitecture of a neuron is very important in defining morphology and ultrastructure. Although there is a wealth of information on the molecular components that make and regulate these ultrastructures, there is a dearth of understanding of ho...