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X-WR-CALDESC:High-Dimensional Statistical Modeling Team Seminar (Mr. Peter 
 Jack NAYLOR)
X-WR-CALNAME:High-Dimensional Statistical Modeling Team Seminar (Mr. Peter 
 Jack NAYLOR)
X-WR-TIMEZONE:Asia/Tokyo
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TZID:Asia/Tokyo
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DTSTART:19700101T000000
TZOFFSETFROM:+0900
TZOFFSETTO:+0900
TZNAME:JST
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BEGIN:VEVENT
UID:801375@techplay.jp
SUMMARY:High-Dimensional Statistical Modeling Team Seminar (Mr. Peter Jack 
 NAYLOR)
DTSTART;TZID=Asia/Tokyo:20201208T153000
DTEND;TZID=Asia/Tokyo:20201208T163000
DTSTAMP:20260511T133642Z
CREATED:20201130T140040Z
DESCRIPTION:イベント詳細はこちら\nhttps://techplay.jp/event/80137
 5?utm_medium=referral&utm_source=ics&utm_campaign=ics\n\nThis is an onlin
 e seminar. Registration is required.\nWe’ll send the instruction for at
 tending the online seminar.\n\n【High-Dimensional Statistical Modeling T
 eam】\n【Date】2020/Dec/8 (Tue)\n\n【Speaker】 Mr. Peter Jack NAYLOR
 \n\n【Title】 \nPredicting from very large images: Application to treat
 ment response in triple-negative breast cancer.\n【Abstract】\nThe rise
  of digital pathology and with it the challenges of histopathology analys
 is have been the focus of a worldwide effort in the overall fight against
  cancer. In parallel\, the recent success of automated decision-making\, 
 machine learning\, and specifically deep learning\, have revolutionized t
 he basis of research as we know today. We tackle the prediction of treatm
 ent response in triple-negative breast cancer patients with two different
  approaches that reach similar outcomes. The first line of approach\, bas
 ed on the recent success of computer vision\, extracts learned features f
 rom the data in order to perform classification. The second line of appro
 ach forces the information flow to pass through nuclei segmentation.\n In
  particular\, it allows the incorporation of biologically relevant high-r
 esolution information on to a lower resolution overview.
LOCATION:オンライン
URL:https://techplay.jp/event/801375?utm_medium=referral&utm_source=ics&utm
 _campaign=ics
END:VEVENT
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