RT: DataSciNews: RT ylecun: ConvNet outperforms human dermatologists for melanoma detection. Dermatologists in level-II protocol: - sensitivity: 88.9% (±9.6%, P = 0.19) - specificity: 75.7% (±11.7%, P < 0.05).... https://t.co/VjnGscpDR1
Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists | Annals of Oncology | Oxford Academic https://t.co/bYLdJikOJG
RT @amyburvall: Pointing out the benefits of something does not erase the need to explore the potential drawbacks. These researchers are at…
Pointing out the benefits of something does not erase the need to explore the potential drawbacks. These researchers are at least looking into a "debiasing antidote". Proactive > Reactive or Defensive https://t.co/KRpW1vxhoI https://t.co/ilck1u1hau
Well I’m glad the good folks at Stanford have a balanced view and are exploring the possible ethical conundrums https://t.co/BBjLaZpi2k https://t.co/l3pleQNOtK
@amyburvall @Ghalamchii @EdTechEurope Research in Oncology is clear - AI is having a successful impact - Melanomas, Lung cancer etc. also in radiology this is how medicine progresses https://t.co/7PGVtFV6K3 https://t.co/FkyEc6vNsC
RT @HelloStartups: Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma rec…
RT @ylecun: ConvNet outperforms human dermatologists for melanoma detection. Dermatologists in level-II protocol: - sensitivity: 88.9% (±…
Mamona and Näînø yeh lo, how cool is that 😉 https://t.co/GsyGJZUmGP
RT @ChikaObuah: Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recogn…
RT @Annals_Oncology: Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma r…
RT @Annals_Oncology: With an @altmetric score of >1200, 'Man against machine' on the detection of melanoma using #AI is a recommended read…
RT @RafikSmati: #SignesDuFutur : une Intelligence Artificielle se montre plus fiable pour diagnostiquer le cancer de la peau que 58 dermato…
RT @RafikSmati: #SignesDuFutur : une Intelligence Artificielle se montre plus fiable pour diagnostiquer le cancer de la peau que 58 dermato…
RT @ylecun: ConvNet outperforms human dermatologists for melanoma detection. Dermatologists in level-II protocol: - sensitivity: 88.9% (±…
RT @ylecun: ConvNet outperforms human dermatologists for melanoma detection. Dermatologists in level-II protocol: - sensitivity: 88.9% (±…
RT @ylecun: ConvNet outperforms human dermatologists for melanoma detection. Dermatologists in level-II protocol: - sensitivity: 88.9% (±…
Dur burda https://t.co/5jK3nG6jqw
RT @ylecun: ConvNet outperforms human dermatologists for melanoma detection. Dermatologists in level-II protocol: - sensitivity: 88.9% (±…
"Diagnostic performance of a deep learning convolutional neural network (CNN) for dermoscopic melanoma recognition in comparison to 58 dermatologists." https://t.co/iG61iPfXIw
RT @ylecun: ConvNet outperforms human dermatologists for melanoma detection. Dermatologists in level-II protocol: - sensitivity: 88.9% (±…
RT @ylecun: ConvNet outperforms human dermatologists for melanoma detection. Dermatologists in level-II protocol: - sensitivity: 88.9% (±…
RT @Annals_Oncology: With an @altmetric score of >1200, 'Man against machine' on the detection of melanoma using #AI is a recommended read…
RT @ylecun: ConvNet outperforms human dermatologists for melanoma detection. Dermatologists in level-II protocol: - sensitivity: 88.9% (±…
RT @Annals_Oncology: With an @altmetric score of >1200, 'Man against machine' on the detection of melanoma using #AI is a recommended read…
With an @altmetric score of >1200, 'Man against machine' on the detection of melanoma using #AI is a recommended read https://t.co/cwPKbCamPN
RT @ylecun: ConvNet outperforms human dermatologists for melanoma detection. Dermatologists in level-II protocol: - sensitivity: 88.9% (±…
"Conclusions For the first time we compared a CNN’s diagnostic performance with a large international group of 58 dermatologists, including 30 experts. Most dermatologists were outperformed by the CNN..." https://t.co/R2ydwgSw30
RT @ylecun: ConvNet outperforms human dermatologists for melanoma detection. Dermatologists in level-II protocol: - sensitivity: 88.9% (±…
RT @MarkHMichalski: Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma re…
RT @ylecun: ConvNet outperforms human dermatologists for melanoma detection. Dermatologists in level-II protocol: - sensitivity: 88.9% (±…
RT @ylecun: ConvNet outperforms human dermatologists for melanoma detection. Dermatologists in level-II protocol: - sensitivity: 88.9% (±…
RT @ylecun: ConvNet outperforms human dermatologists for melanoma detection. Dermatologists in level-II protocol: - sensitivity: 88.9% (±…
RT @ylecun: ConvNet outperforms human dermatologists for melanoma detection. Dermatologists in level-II protocol: - sensitivity: 88.9% (±…
RT @ylecun: ConvNet outperforms human dermatologists for melanoma detection. Dermatologists in level-II protocol: - sensitivity: 88.9% (±…
RT @ylecun: ConvNet outperforms human dermatologists for melanoma detection. Dermatologists in level-II protocol: - sensitivity: 88.9% (±…
RT @ylecun: ConvNet outperforms human dermatologists for melanoma detection. Dermatologists in level-II protocol: - sensitivity: 88.9% (±…
RT @ylecun: ConvNet outperforms human dermatologists for melanoma detection. Dermatologists in level-II protocol: - sensitivity: 88.9% (±…
@AustinTanney starting to become a familiar story - great to see. https://t.co/SEomevY7hr
RT @ylecun: ConvNet outperforms human dermatologists for melanoma detection. Dermatologists in level-II protocol: - sensitivity: 88.9% (±…
RT @ylecun: ConvNet outperforms human dermatologists for melanoma detection. Dermatologists in level-II protocol: - sensitivity: 88.9% (±…
RT @ylecun: ConvNet outperforms human dermatologists for melanoma detection. Dermatologists in level-II protocol: - sensitivity: 88.9% (±…
RT @ylecun: ConvNet outperforms human dermatologists for melanoma detection. Dermatologists in level-II protocol: - sensitivity: 88.9% (±…
RT @ylecun: ConvNet outperforms human dermatologists for melanoma detection. Dermatologists in level-II protocol: - sensitivity: 88.9% (±…
stanfordnlp: RT DrLukeOR: I wanted to finish talking to the journal, but the media articles keep coming and even ylecun is repeating the claims... time for some #openpeerreview! This paper is flawed. Their comparison underestimates human performance sys…
#stanfordnlp RT DrLukeOR: I wanted to finish talking to the journal, but the media articles keep coming and even ylecun is repeating the claims... time for some #openpeerreview! This paper is flawed. Their comparison underestimates human performance sys…
RT @ylecun: ConvNet outperforms human dermatologists for melanoma detection. Dermatologists in level-II protocol: - sensitivity: 88.9% (±…
RT @ylecun: ConvNet outperforms human dermatologists for melanoma detection. Dermatologists in level-II protocol: - sensitivity: 88.9% (±…
The journals are just playing catch up with what’s happening in the startup scene https://t.co/tuV5pdzoY1
RT @ylecun: ConvNet outperforms human dermatologists for melanoma detection. Dermatologists in level-II protocol: - sensitivity: 88.9% (±…
RT @FAndreMD: This paper has an altimetric score >1000 <2 weeks after release. certainly one of the "must read" in the medical literature i…
RT @ylecun: ConvNet outperforms human dermatologists for melanoma detection. Dermatologists in level-II protocol: - sensitivity: 88.9% (±…
RT @ylecun: ConvNet outperforms human dermatologists for melanoma detection. Dermatologists in level-II protocol: - sensitivity: 88.9% (±…
RT @ylecun: ConvNet outperforms human dermatologists for melanoma detection. Dermatologists in level-II protocol: - sensitivity: 88.9% (±…
RT @ylecun: ConvNet outperforms human dermatologists for melanoma detection. Dermatologists in level-II protocol: - sensitivity: 88.9% (±…
RT @FAndreMD: Detection of melanoma using AI. Impact of AI could be huge since it could allow equal access to high quality cancer diagnosis…
RT @ylecun: ConvNet outperforms human dermatologists for melanoma detection. Dermatologists in level-II protocol: - sensitivity: 88.9% (±…
RT @AdvancingDerm: #Dermatologist against Machine. #AI diagnosis of #melanoma https://t.co/zCwyQYTy0J #Google Inception V4 CNN