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DTSTAMP:20231209T132000Z
UID:55703254-5A20-49CA-AE75-49CCC6409EE2
DTSTART;TZID=America/New_York:20231206T100000
DTEND;TZID=America/New_York:20231206T110000
DESCRIPTION:Microwave breast imaging is a promising frontier in breast canc
 er detection. This presentation summarizes over a decade of diverse studie
 s\, from early beginnings to a prototype portable device for underserved c
 ommunities. We&#39;ve developed a Health-Canada-approved device using 24 small
  vector network analyzers. Ongoing efforts focus on a low-cost multistatic
  device. Disparities in breast cancer detection for marginalized populatio
 ns highlight the need for the system&#39;s low-cost\, portable\, and user-frie
 ndly solutions. A critical review emphasizes the need to improve specifici
 ty for BMI. High-contrast image reconstruction methods sometimes overshado
 w accuracy\, a key area for investigation. The presentation focuses on rad
 ar-based reconstruction algorithms\, addressing various challenges. It int
 roduces a differential dual-breast diagnostic strategy for more accurate e
 stimates. In conclusion\, a decade of research underscores ongoing learnin
 g and integration for advancements\, crucial for impacting early breast ca
 ncer detection in remote locations and low-middle income countries.\n\nRoo
 m: ENGMC603\, Bldg: McConnell Engineering Building\, 3480 Rue University\,
  Montreal\, Quebec\, Canada
LOCATION:Room: ENGMC603\, Bldg: McConnell Engineering Building\, 3480 Rue U
 niversity\, Montreal\, Quebec\, Canada
ORGANIZER:milad.mokhtari@mail.mcgill.ca
SEQUENCE:17
SUMMARY:A Decade+ of Breast Microwave Imaging (BMI): Lessons Learned and Fu
 ture Directions
URL;VALUE=URI:https://events.vtools.ieee.org/m/387986
X-ALT-DESC:Description: &lt;br /&gt;&lt;p&gt;Microwave breast imaging is a promising fr
 ontier in breast cancer detection. This presentation summarizes over a dec
 ade of diverse studies\, from early beginnings to a prototype portable dev
 ice for underserved communities. We&#39;ve developed a Health-Canada-approved 
 device using 24 small vector network analyzers. Ongoing efforts focus on a
  low-cost multistatic device. Disparities in breast cancer detection for m
 arginalized populations highlight the need for the system&#39;s low-cost\, por
 table\, and user-friendly solutions. A critical review emphasizes the need
  to improve specificity for BMI. High-contrast image reconstruction method
 s sometimes overshadow accuracy\, a key area for investigation. The presen
 tation focuses on radar-based reconstruction algorithms\, addressing vario
 us challenges. It introduces a differential dual-breast diagnostic strateg
 y for more accurate estimates. In conclusion\, a decade of research unders
 cores ongoing learning and integration for advancements\, crucial for impa
 cting early breast cancer detection in remote locations and low-middle inc
 ome countries.&lt;/p&gt;
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