Integrating AI with Medical Imaging in the Healthcare Domain
Medical imaging is transforming the landscape of oncology by playing a critical role in the process of cancer detection and therapeutic approaches. Medical experts may visualize the internal architecture of the body with unmatched precision using modern technologies such as MRI, X-rays, CT scans, Ultrasound and PET scans. Imaging techniques in cancer identification allow for the early detection of aberrant tissue growth, assisting in the prompt diagnosis and characterization of various tumours. Medical imaging also aids in treatment planning by providing detailed information regarding the size, location, and nature of malignant lesions. The introduction of machine learning in medical imaging has been a transformational force, as it changes how healthcare workers evaluate and interpret complex data. Deep learning, specifically, has proven critical in tasks such as image segmentation, classification, and detection, considerably improving diagnostic accuracy. Deep learning algorithms, particularly convolutional neural networks (CNNs), excel at learning extensive patterns and characteristics from large datasets, allowing them to identify subtle abnormalities in medical images that the human eye may not pick up on. This technology has considerably increased the speed and precision of diagnostic processes, enabling earlier and more precise diagnosis of diseases, namely cancer. Deep learning integration in various regions of medical imaging represents a paradigm shift, enabling innovations with the potential to reshape the healthcare field, leading to more efficient and effective patient care in the long run, as will be further discussed.
Date and Time
Location
Hosts
Registration
- Date: 28 Nov 2023
- Time: 03:00 PM to 04:00 PM
- All times are (UTC-05:00) Eastern Time (US & Canada)
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- 20 Carlton Street
- Toronto, Ontario
- Canada
- Building: The Carlton Cinema (CAR)
- Room Number: CAR 09
Speakers
Maeashah of Toronto Metropolitan University
Biography:
Maeashah Haque, BEng.
Maeashah Haque earned her bachelor's degree in Biomedical Engineering from Toronto Metropolitan University in 2023. Following her graduation, she has been working as a research assistant at the Biomedical Optics and Ultrasound Laboratory. Her primary research revolves around semantic segmentation, image processing, and the use of AI in medical imaging.
During her time at TMU, Maeashah actively participated in various leadership roles within the Biomedical Engineering Course Union, Biomedical Engineering Society, Engineering Outreach team, Eureka (STEM discovery camp), and the Tri-Mentoring Program. Additionally, she contributed to the Math Support Department at SLS. Beyond her extracurricular commitments, Maeashah successfully completed a 16-month internship at ZOLL Medical, a company specializing in the development of medical devices and software solutions for emergency care.