Talks with Prof. Hugo Aerts, Prof. Hayit Greenspan, Prof. Haotian Lin, Prof. Kang Zhang and Prof. Baiying Lei, chaired by Dr Elena Bellafante, Senior Scientific Editor, Med Recorded at the BMSTC-Cell Press joint conference “The future of medicine: development and applications of AI in disease biology and health care”. Video starts at 00:00 Prof. Hugo Aerts (starts at 00:03) Topic: Artificial intelligence for cancer imaging Prof. Hayit Greenspan (starts at 27:43) Topic: AI in medical imaging for clinical decision making Prof. Haotian Lin (starts at 55:19) Topic: Artificial intelligence in ophthalmology: A window into systemic diseases Prof. Kang Zhang (starts at 01:08:43) Topic: Clinical diagnosis and outcome predictions enabled by big data and AI Prof. Baiying Lei (starts at 01:41:49) Topic: Multi-time multi-modal neuroimage learning for brain disease diagnosis
(26 Jan 2020) LEAD IN: Researchers in Taiwan are combining machine learning through artificial intelligence (AI) with medical imaging to help diagnose tumors more quickly. They say  AI computing reduces of the time required to diagnose a tumor, from days to under ten minutes. This allows patients to be treated earlier, improving their chances of recovery.   STORY-LINE These AI Labs are working with Taipei Medical University Hospital to develop tools which cuts the time it takes doctors to diagnose cancers. An advance which could benefit patients. Ethan Tu is the founder of Taiwan AI Labs. He explains how the new AI platform learns to read the medical images: “We actually learn how to segment a liver out of the CT scan slides from the open data or only a few data set. Then we use an interactive way to automatically learn from the doctor how to segment the tumor out of the CT scan. And through this interactive approach, we don’t need a huge amount of medical images. We only need a small amount of medical images to start with. Then using the interactive way, the computer will learn from the doctor over  time.” Medical imaging is a technique of creating images of organs and tissues inside a body allowing doctors to investigate whether medical intervention is necessary. Machine learning is when a set of techniques and algorithms enable computers to discover complicated patterns in large data sets. They team says the old method of using CT scans to detect tumours meant [More]
Philips and PathAI team up to improve breast cancer diagnosis using artificial intelligence technology in ‘big data’ pathology research. Royal Philips, a global leader in health technology, and PathAI, a company that develops artificial intelligence technology for pathology, are collaborating with the aim to develop solutions that improve the precision and accuracy of routine diagnosis of cancer and other diseases. The partnership aims to build deep learning applications in computational pathology enabling this form of artificial intelligence to be applied to massive pathology data sets to better inform diagnostic and treatment decisions. The initial focus of this effort is on developing applications to automatically detect and quantify cancerous lesions in breast cancer tissue. transcript: #ibelieve – Breast cancer is one of the largest medical problems faced today. One in eight women will be diagnosed with breast cancer at some point in their lifetime. Therefore it is an area where building new tools to more effectively diagnose the disease and cure the disease can have a major impact on a large proportion of the population. So the central mission of pathology has not change that much in the past few hundred years. It has always been to provide the most useful diagnosis for the patient. But what has changed tremendously is the amount of data at our disposal about the patient about the tissue sample including things like genomic data, transcriptome data, lots of different morphologic data types. And the goal of computational pathology is to enable pathologists to most effectively [More]
Medical diagnosis often is based on information visible from medical images. But what if machine learning and artificial intelligence could help our doctors gain more knowledge from medical images and therefore predict diseases earlier? New research indicates this is indeed possible, and the implications for preventive medicine and several disease types are large. Learn about it in this talk. Shinjini graduated from high school at age 16, earned a PhD at 25, and was named one of Pittsburgh’s 40 under 40 in 2016. In her PhD work at Stanford, Shinjini employed machine learning that trained computers to detect patterns in medical images. That technology enables doctors to detect osteoarthritis three years before symptoms are evident. Shinjini is currently an MD-PhD scientist in the Medical Scientist Training Program (MSTP), a collaboration between the University of Pittsburgh and Carnegie Mellon University. Her research focuses on medical diagnosis in the pre-disease stage when a patient is still asymptomatic. She is also an advocate for women and minorities in STEM professions. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at