Medical Image Analysis
Augmenting human intelligence for improved medical decision-making
LIST OF FUNCTIONALITIES
- Augmenting human capabilities in medical image analysis and supporting the decision-making process
- Quantification of findings and increased diagnostic quality
- Integration of clinical and -omics information into combined analysis of patient datasets
USED TECHNOLOGIES
- Deep Learning and classical machine learning approaches for detection, classification and semantic segmentation
- Semi-supervised learning for robustness and data efficiency
- Network and decision visualization to increase explainability and trust in the solution
- Integrated analysis of heterogeneous data sources
LIST OF FUNCTIONALITIES
- Augmenting human capabilities in medical image analysis and supporting the decision-making process
- Quantification of findings and increased diagnostic quality
- Integration of clinical and -omics information into combined analysis of patient datasets
USED TECHNOLOGIES
- Deep Learning and classical machine learning approaches for detection, classification and semantic segmentation
- Semi-supervised learning for robustness and data efficiency
- Network and decision visualization to increase explainability and trust in the solution
- Integrated analysis of heterogeneous data sources
KONICA MINOLTA DIGITAL SERVICES R&D SOLUTION
Konica Minolta develops solutions to augment the human experts in decision-making for clinical and preclinical applications. In order to bring deep learning into medical practice, we’re investigating novel approaches to improve robustness, transparency and data-efficiency of our functionalities.
BETTER DECISION MAKING FOR MEDICAL PRACTITIONERS
Medical Imaging plays a crucial role in the detection and diagnosis of disease, and in the assessment and decision of the appropriate treatment, but also in the preclinical research and clinical trials required to create new therapies. However, the amount of information in medical images such as X-Ray, CT, MRI, Ultrasound and Histopathology exceeds the capabilities of human experts to process under time restrictions everything that may be relevant. Computer solutions can augment the human as a second reader, looking for the tiniest signs that could be easily overlooked. They can analyse, quantify images and therefore support decision-making and can be used to correlate image characteristics with disease outcomes. Our solutions in digital pathology facilitate the workflow of clinical pathologists as well as preclinical researchers. We automate the cumbersome work and facilitate the pathological assessment through quantification and analysis.
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INSIGHTS FROM DATA:
AI FOR MEDICAL IMAGING
Digital transformation holds particular promise for healthcare systems throughout the world. The digitalisation of medical data management allows for easy recording, exchange and accessibility, whilst improving interoperability between healthcare providers.
Within healthcare, the Cognitive Hub architecture integrates all medical information and orchestrates the intelligent systems that provide insight and decision support based on this data.
APPLICATIONS
For radiological image analysis, applications include disease detection, supporting the physicians as a second reader, or prioritizing image reading based on the analysed image content.
APPLICATIONS
For radiological image analysis, applications include disease detection, supporting the physicians as a second reader, or prioritizing image reading based on the analysed image content.
MORE INFO
Email to research@konicaminolta.eu
