Proactive Construction of an Annotated Imaging Database for Artificial Intelligence Training.

Artificial intelligence (AI) holds much promise for enabling highly desired imaging diagnostics improvements. One of the most limiting bottlenecks for the development of useful clinical-grade AI models is the lack of training data. One aspect is the large amount of cases needed and another is the necessity of high-quality ground truth annotation.

TissueWand, a Rapid Histopathology Annotation Tool.

Background: Recent advancements in machine learning (ML) bring great possibilities for the development of tools to assist with diagnostic tasks within histopathology. However, these approaches typically require a large amount of ground truth training data in the form of image annotations made by human experts. As such annotation work is a very time-consuming task, there is a great need for tools that can assist in this process, saving time while not sacrificing annotation quality.

Annotations, Ontologies, and Whole Slide Images - Development of an Annotated Ontology-Driven Whole Slide Image Library of Normal and Abnormal Human Tissue.

Objective: Digital pathology is today a widely used technology, and the digitalization of microscopic slides into whole slide images (WSIs) allows the use of machine learning algorithms as a tool in the diagnostic process. In recent years, "deep learning" algorithms for image analysis have been applied to digital pathology with great success. The training of these algorithms requires a large volume of high-quality images and image annotations.

Clinical experience of photon counting breast tomosynthesis: comparison with traditional mammography.

Background: In two-dimensional mammography, a well-known problem is over- and underlying tissue which can either obstruct a lesion or create a false-positive result. Tomosynthesis, with an ability to layer the tissue in the image, has the potential to resolve these issues.

Purpose: To compare the diagnostic quality, sensitivity and specificity of a single tomosynthesis mammography image and a traditional two-view set of two-dimensional mammograms and to assess the comfort of the two techniques.

Optimization of a dual-energy contrast-enhanced technique for a photon-counting digital breast tomosynthesis system: I. A theoretical model.

Purpose: Dual-energy (DE) iodine contrast-enhanced x-ray imaging of the breast has been shown to identify cancers that would otherwise be mammographically occult. In this article, theoretical modeling was performed to obtain optimally enhanced iodine images for a photon-counting digital breast tomosynthesis (DBT) system using a DE acquisition technique.

Methods: In the system examined, the breast is scanned with a multislit prepatient collimator aligned with a multidetector camera.