Gianmaria Silvello invited speaker at the second Swetaly workshop on AI, will describe #ExaMode and the latest advancements of the project at 11:00am
https://t.co/VJfBMezOE1
The ExaMode results on "training computer-aided diagnosis models without human annotations" are on a spotlight on the Nature Health Portfolio @NaturePortfolio https://t.co/1n1LWwf9DS
The approach is independent of the tissue and type of images analyzed. It can be replicated on data coming from different organs (lungs, prostate, head) and, most importantly, on different types of images.
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The @examode consortium's latest result on cancer diagnostics models for empowering digital pathology by limiting human effort to a minimum: https://t.co/txIZTSzxSE
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The implications are potentially disruptive: the need for medical experts to analyze and annotate healthcare data might be removed, leading to unprecedented possibilities in exploiting data already available in hospitals.
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The combination of clinical healthcare data with #AI technologies leads to new algorithms assisting the #diagnosis and the #decisionmaking, even for #cancer. This revolution has been slowed by the requirement of #manualannotations to train AI systems with prohibitive costs.
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+++ Breaking news +++
"Unleashing the potential of digital pathology data by training computer-aided diagnosis models without human annotations" is out on #DigitalMedicine by @SpringerNature (gold #openaccess).
https://t.co/txIZTSzxSE
Today at the "Multilingual Digital Terminology Today MDTT 2022", there will be a talk about @examode and #terminology:
"Terminology Extraction in Electronic Health Records. The ExaMode Project" by
@airamoigroig@sfn_mrc and @giansilv
https://t.co/GRQHuEa1Bp
Check out the latest #EXAMODE blog post on "Training Biomedical #RelationExtraction Models with a Limited amount of Annotated Data".
https://t.co/wnltPEhfaG
ExaMode is sponsoring #TPDL2022 and supporting the keynotes. Check out this great keynote about software preservation a topic relevant to all fields related and connected to #computerScience
TBGA a large scale #gene#disease association (#GDA) dataset for training #relationExtraction algorithms, available as #opendata: https://t.co/qCxRpb5Z48
Details in the recent #openaccess paper in @BMCBioinfo by @sfn_mrc and @giansilv https://t.co/ocEuu4O5NU
ExaMode in liaison with https://t.co/T0XjewNYvl just formally started a collaboration on Computational Pathology terminology. Check it out: https://t.co/aWziBkJoun
Check put our latest blog post on Training #Cancer Diagnostics Models for #Histopathology without Human Intervention: When #NLP meets #ComputerVision
https://t.co/PzzBaB1Ues