AIMC Topic: Image Processing, Computer-Assisted

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Optimizing deep learning-based segmentation of densely packed cells using cell surface markers.

BMC medical informatics and decision making
BACKGROUND: Spatial molecular profiling depends on accurate cell segmentation. Identification and quantitation of individual cells in dense tissues, e.g. highly inflamed tissue caused by viral infection or immune reaction, remains a challenge.

Breast density prediction from low and standard dose mammograms using deep learning: effect of image resolution and model training approach on prediction quality.

Biomedical physics & engineering express
. To improve breast cancer risk prediction for young women, we have developed deep learning methods to estimate mammographic density from low dose mammograms taken at approximately 1/10th of the usual dose. We investigate the quality and reliability ...

An intelligent wireless channel corrupted image-denoising framework using symmetric convolution-based heuristic assisted residual attention network.

Network (Bristol, England)
Image denoising is one of the significant approaches for extracting valuable information in the required images without any errors. During the process of image transmission in the wireless medium, a wide variety of noise is presented to affect the im...

An invisible, robust copyright protection method for DNN-generated content.

Neural networks : the official journal of the International Neural Network Society
Wide deployment of deep neural networks (DNNs) based applications (e.g., style transfer, cartoonish), stimulating the need for copyright protection of such application's production. Though some traditional visible copyright techniques exist, they oft...

Impact of imperfect annotations on CNN training and performance for instance segmentation and classification in digital pathology.

Computers in biology and medicine
Segmentation and classification of large numbers of instances, such as cell nuclei, are crucial tasks in digital pathology for accurate diagnosis. However, the availability of high-quality datasets for deep learning methods is often limited due to th...

Large-scale 3D non-Cartesian coronary MRI reconstruction using distributed memory-efficient physics-guided deep learning with limited training data.

Magma (New York, N.Y.)
OBJECT: To enable high-quality physics-guided deep learning (PG-DL) reconstruction of large-scale 3D non-Cartesian coronary MRI by overcoming challenges of hardware limitations and limited training data availability.

Quality-driven deep cross-supervised learning network for semi-supervised medical image segmentation.

Computers in biology and medicine
Semi-supervised medical image segmentation presents a compelling approach to streamline large-scale image analysis, alleviating annotation burdens while maintaining comparable performance. Despite recent strides in cross-supervised training paradigms...

Cardiac function in a large animal model of myocardial infarction at 7 T: deep learning based automatic segmentation increases reproducibility.

Scientific reports
Cardiac magnetic resonance (CMR) imaging allows precise non-invasive quantification of cardiac function. It requires reliable image segmentation for myocardial tissue. Clinically used software usually offers automatic approaches for this step. These ...

Deep learning with diffusion MRI as in vivo microscope reveals sex-related differences in human white matter microstructure.

Scientific reports
Biological sex is a crucial variable in neuroscience studies where sex differences have been documented across cognitive functions and neuropsychiatric disorders. While gross statistical differences have been previously documented in macroscopic brai...

ERSegDiff: a diffusion-based model for edge reshaping in medical image segmentation.

Physics in medicine and biology
Medical image segmentation is a crucial field of computer vision. Obtaining correct pathological areas can help clinicians analyze patient conditions more precisely. We have observed that both CNN-based and attention-based neural networks often produ...