AIMC Topic: Image Processing, Computer-Assisted

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The future of pathology is digital.

Pathology, research and practice
Information, archives, and intelligent artificial systems are part of everyday life in modern medicine. They already support medical staff by mapping their workflows with shared availability of cases' referral information, as needed for example, by t...

Task-based characterization of a deep learning image reconstruction and comparison with filtered back-projection and a partial model-based iterative reconstruction in abdominal CT: A phantom study.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: We aimed to thoroughly characterize image quality of a novel deep learning image reconstruction (DLIR), and investigate its potential for dose reduction in abdominal CT in comparison with filtered back-projection (FBP) and a partial model-ba...

Deep Learning of Cancer Stem Cell Morphology Using Conditional Generative Adversarial Networks.

Biomolecules
Deep-learning workflows of microscopic image analysis are sufficient for handling the contextual variations because they employ biological samples and have numerous tasks. The use of well-defined annotated images is important for the workflow. Cancer...

Machine learning and natural language processing methods to identify ischemic stroke, acuity and location from radiology reports.

PloS one
Accurate, automated extraction of clinical stroke information from unstructured text has several important applications. ICD-9/10 codes can misclassify ischemic stroke events and do not distinguish acuity or location. Expeditious, accurate data extra...

SpeckleGAN: a generative adversarial network with an adaptive speckle layer to augment limited training data for ultrasound image processing.

International journal of computer assisted radiology and surgery
PURPOSE: In the field of medical image analysis, deep learning methods gained huge attention over the last years. This can be explained by their often improved performance compared to classic explicit algorithms. In order to work well, they need larg...

DDeep3M: Docker-powered deep learning for biomedical image segmentation.

Journal of neuroscience methods
BACKGROUND: Deep learning models are turning out to be increasingly popular in biomedical image processing. The fruitful utilization of these models, in most cases, is substantially restricted by the complicated configuration of computational environ...

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis.

Journal of visualized experiments : JoVE
Glomerular cell death is a pathological feature of myeloperoxidase anti neutrophil cytoplasmic antibody associated vasculitis (MPO-AAV). Extracellular deoxyribonucleic acid (ecDNA) is released during different forms of cell death including apoptosis,...

Diabetic Retinopathy Screening with Automated Retinal Image Analysis in a Primary Care Setting Improves Adherence to Ophthalmic Care.

Ophthalmology. Retina
PURPOSE: Retinal screening examinations can prevent vision loss resulting from diabetes but are costly and highly underused. We hypothesized that artificial intelligence-assisted nonmydriatic point-of-care screening administered during primary care v...

Deep learning-based survival prediction for multiple cancer types using histopathology images.

PloS one
Providing prognostic information at the time of cancer diagnosis has important implications for treatment and monitoring. Although cancer staging, histopathological assessment, molecular features, and clinical variables can provide useful prognostic ...

Multi-input deep learning architecture for predicting breast tumor response to chemotherapy using quantitative MR images.

International journal of computer assisted radiology and surgery
PURPOSE: Neoadjuvant chemotherapy (NAC) aims to minimize the tumor size before surgery. Predicting response to NAC could reduce toxicity and delays to effective intervention. Computational analysis of dynamic contrast-enhanced magnetic resonance imag...