Deep Learning-Based Nuclear Morphometry Reveals an Independent Prognostic Factor in MCL
A New Era.
AI - empowered Digital Pathology
Pathology is the study of disease. Until recent years, the traditional practice of pathology has remained largely unchanged over its 150-year history. This traditional practice is not only manual, but is prone to inaccuracy, error, and inefficiency.
Advances in technological development and the disruption caused by the COVID-19 pandemic mean the need for digital pathology has taken on greater urgency. However, a digital transformation of healthcare without comprehensive AI integration will remain incomplete and of limited scope. Pathologists need the insights of AI-assisted data analysis to improve the efficiency and accuracy of their diagnostic support.
AI - driven Digital Transformation
aetherAI provides solutions for digital pathology and AI-powered diagnostic support to help pathologists perform more effectively, realizing the true potential of the digital transformation.
aetherAI is committed to developing web-based digital pathology platforms for clinical reporting and AI research and development. Our offering includes a web-based digital pathology image management and viewer system, image annotation services, AI model construction, and an integrated AI-powered workflow. aetherAI takes comprehensive care of all digital pathology AI development needs. In collaboration with aetherAI, you can take advantage of the following features.
Instant image sharing
and better collaboration
Focused on improving efficiency and productivity,
the Digital Pathology Workflow System is designed around the pathologist’s daily workflow. With easy monitoring of workload and process, the AI-integrated workflow can help you prioritize work and focus on the most important tasks.
aetherAI seeks to elevate the standard of pathology diagnosis via the superior image recognition capacities of deep neural networks. Our offerings range from slide quality control to case triaging, differential cell counting, and IHC quantification.
True GigaPixel AI
Extreme resolution of whole-slide images (WSI) means dealing with ten billion pixels at a time, and such huge data loads can easily trigger GPU out-of-memory errors during CNN training. While ordinary patch-based workarounds create the extra burden of writing detailed annotations, aetherAI’s True GigaPixel AI takes an annotation-free training approach by using entire WSIs and slide-level diagnoses.
True GigaPixel AI not only overcomes memory constraints but also dramatically speeds up the pathology AI development process by alleviating the burden of contouring.
This breakthrough method can scale up training with numerous WSIs and existing slides for AI applications.
Integrated AI Systems
aetherAI augments medical imaging devices with integrated AI solutions, bringing intelligence into clinical workflow.