BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/New_York BEGIN:DAYLIGHT DTSTART:20240310T030000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:EDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20231105T010000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:EST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20231201T141356Z UID:1E46399D-A4A1-423B-8C39-DA5E44154C00 DTSTART;TZID=America/New_York:20231128T150000 DTEND;TZID=America/New_York:20231128T160000 DESCRIPTION:Medical imaging is transforming the landscape of oncology by pl aying a critical role in the process of cancer detection and therapeutic a pproaches. Medical experts may visualize the internal architecture of the body with unmatched precision using modern technologies such as MRI\, X-ra ys\, CT scans\, Ultrasound and PET scans. Imaging techniques in cancer ide ntification allow for the early detection of aberrant tissue growth\, assi sting in the prompt diagnosis and characterization of various tumours. Med ical imaging also aids in treatment planning by providing detailed informa tion regarding the size\, location\, and nature of malignant lesions. The introduction of machine learning in medical imaging has been a transformat ional 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 co nvolutional 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. Th is technology has considerably increased the speed and precision of diagno stic processes\, enabling earlier and more precise diagnosis of diseases\, namely cancer. Deep learning integration in various regions of medical im aging represents a paradigm shift\, enabling innovations with the potentia l to reshape the healthcare field\, leading to more efficient and effectiv e patient care in the long run\, as will be further discussed.\n\nSpeaker( s): Maeashah\, \n\nRoom: CAR 09\, Bldg: The Carlton Cinema (CAR) \, 20 Car lton Street\, Toronto\, Ontario\, Canada LOCATION:Room: CAR 09\, Bldg: The Carlton Cinema (CAR) \, 20 Carlton Street \, Toronto\, Ontario\, Canada ORGANIZER:ofalou@gmail.com SEQUENCE:3 SUMMARY:Integrating AI with Medical Imaging in the Healthcare Domain URL;VALUE=URI:https://events.vtools.ieee.org/m/386101 X-ALT-DESC:Description: <br /><p>&nbsp\;</p>\n<p><span style="font-weight: 400\;">Medical imaging is transforming the landscape of oncology by playin g a critical role in the process of cancer detection and therapeutic appro aches.&nbsp\; Medical experts may visualize the internal architecture of t he 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\, a ssisting in the prompt diagnosis and characterization of various tumours. Medical imaging also aids in treatment planning by providing detailed info rmation regarding the size\, location\, and nature of malignant lesions. T he introduction of machine learning in medical imaging has been a transfor mational force\, as it changes how healthcare workers evaluate and interpr et complex data. Deep learning\, specifically\, has proven critical in tas ks such as image segmentation\, classification\, and detection\, considera bly improving diagnostic accuracy. Deep learning algorithms\, particularly convolutional neural networks (CNNs)\, excel at learning extensive patter ns and characteristics from large datasets\, allowing them to identify sub tle abnormalities in medical images that the human eye may not pick up on. This technology has considerably increased the speed and precision of dia gnostic processes\, enabling earlier and more precise diagnosis of disease s\, namely cancer. Deep learning integration in various regions of medical imaging represents a paradigm shift\, enabling innovations with the poten tial to reshape the healthcare field\, leading to more efficient and effec tive patient care in the long run\, as will be further discussed. </span>< /p> END:VEVENT END:VCALENDAR