BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//pretalx//pretalx.devconf.info//devconf-us-2026//speaker//QEKWW7
BEGIN:VTIMEZONE
TZID:EST
BEGIN:STANDARD
DTSTART:20001029T030000
RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10;UNTIL=20061029T070000Z
TZNAME:EST
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
END:STANDARD
BEGIN:STANDARD
DTSTART:20071104T030000
RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11
TZNAME:EST
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
END:STANDARD
BEGIN:DAYLIGHT
DTSTART:20000402T030000
RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=4;UNTIL=20060402T080000Z
TZNAME:EDT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
END:DAYLIGHT
BEGIN:DAYLIGHT
DTSTART:20070311T030000
RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3
TZNAME:EDT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
END:DAYLIGHT
END:VTIMEZONE
BEGIN:VEVENT
UID:pretalx-devconf-us-2026-P7GAD7@pretalx.devconf.info
DTSTART;TZID=EST:20260925T094000
DTEND;TZID=EST:20260925T095500
DESCRIPTION:Most skin cancer AI models are trained on perfect\, high-resolu
 tion images taken by dermatologists in a clinic. But in the real world\, p
 eople just take pictures of their skin with their phones\, which are often
  blurry\, poorly lit\, or off-centered. This lightning talk is about build
 ing an AI screening tool that actually works with the messy photos regular
  people take.\n\n\nI will be talking about using the new SLICE-3D dataset 
 (from the ISIC 2024 Grand Challenge) to train a machine learning model tha
 t classifies skin lesions. I'll cover my process for tackling the gap betw
 een clinical data and smartphone images\, including exploring lightweight 
 models like CNNs with transfer learning and quantized Vision Transformers\
 , and discussing the trade-offs between cloud deployment and keeping it as
  a local\, privacy-first tool.\n\nFinally\, I will show a quick live demo 
 of an easy-to-use web interface for the model. The goal isn't to replace d
 octors\, but to create a fast\, unintimidating pre-screening app that can 
 look at a phone picture and tell a user\, "Hey\, this looks irregular\, yo
 u should probably go see a dermatologist."
DTSTAMP:20260727T165257Z
LOCATION:Ladd Room (Capacity 170)
SUMMARY:Real-World Skin Disease Prediction: Bridging the Gap from Clinical 
 Data to Smartphone Screens - Sun Cho
URL:https://pretalx.devconf.info/devconf-us-2026/talk/P7GAD7/
END:VEVENT
END:VCALENDAR