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From: BeenRetired11/28/2020 7:28:01 AM
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NCKU team develops digital imaging solutions in liver pathology leveraging AI technology
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Friday 27 November 2020

The pathologist, specialized in looking at tissue or pathological sections through a microscope, is to make a definitive cancer diagnosis and may also work with physicians or other members of healthcare team to recommend a treatment strategy that could include observation, surgery, chemotherapy, radiation therapy, or a combination of these approaches. The pathologist is indispensable to the cancer care team.

The interpretation of various pathological sections need to use different tissue marking dyes to find more details of each disease. The regular process will need to use different lenses to magnify objects for carefully observation in viewing macro or micro structure under microscope for a pathologist. It will consume lots of time to identify, count and examine imaging data including Liver fibrosis, Tumor, Hepatic necrosis area, liver portal vein infiltration, and fatty liver detection.

Pau-Choo Chung, professor of Department of Electrical Engineering, National Cheng Kung University (NCKU), leads the team to develop digital pathology using image-based solution for the acquisition, management and interpretation of pathology information supported by computational techniques for data extraction and analysis through artificial intelligence (AI) technology. Her pioneer projects are aiming to build an assistant digital pathology system for helping pathologists to save time and efforts to do jobs.

The single digital image file of pathological sections is huge sized data sets ranging from 10 to 50 gigabytes. It is necessary to transform the big image file to several smaller and regional image files for further processing. To perform AI training will require massive compute power. Those big data sets require huge GPU computing power and memory allocation. Through the support by National Center for High-Performance Computing (NCHC), this project gets support to access Taiwan Computing Cloud (TWCC) platform to perform the AI Training. The current stage for doing the AI model development, the data set is accumulated around 4000 images by the collaboration with National Cheng Kung University Hospital (NCKUH) and Kaohsiung Veterans General Hospital.

For expanding to more use cases after performing AI training process, the project team uses TWCC cloud services to get increases in inference efficiency to fit the requirements of time constraints in hospital. However, it is requiring to link to the service of TWCC for leveraging the AI models under NCHC reinforced cyber-security protection. Some of the hospitals worry patient's data breach, there is another solution to build compute and storage servers inside the hospital's IT infrastructure. Currently the NCKUH and Kaohsiung Veterans General Hospital join force together to deploy this AI system into use as the standard operation procedures. Professor Chung welcomes these investments. And this will help the AI assistant system having further use in the hospitals.

The project teams of Professor Chung are heavy users of TWCC services especially in AI training process. The data sets are huge objects to be uploaded to the cloud data center servers. That's the reason why both project team members and TWCC technical support team members frequently to conduct meeting having discussion of ideas for better usage of the TWCC services. It is a great appreciation to see the huge time saving for the project team members.

The next steps of project teams are pushing the cloud services of liver pathology assistance systems under carefully considering cyber security priorities. This will help to expand more usage to all Taiwan hospitals with local storage of sensitive medical data. The entire design is aiming to apply TFDA license as a key milestone for making it commercially available in Taiwan. For achieving this goal, it is very important to collaborate with TWCC services. The TWCC service is an invisible hero, said Professor Chung with highly appreciation.
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