#Dermatologist against Machine. #AI diagnosis of #melanoma https://t.co/zCwyQYTy0J #Google Inception V4 CNN
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% (±…
Retweeted Yann LeCun (@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/oYProFwVF0
RT @ylecun: ConvNet outperforms human dermatologists for melanoma detection. Dermatologists in level-II protocol: - sensitivity: 88.9% (±…
If your kids are going into medicine soon, perhaps radiology may not be a great choice. Outside healthcare, ML is eating the perception and structured data prediction world already! https://t.co/xWiVIOHLQn
Deri kanseri tanısında dermatologlara karşı yapay zekanın başarısı; https://t.co/DSenmER5ZD
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% (±…
Only a matter of time before deep disease diagnosis will rule the roost.. Convolutional nets bests 58 dermatologists in melanoma diagnosis.. https://t.co/F6TvfYW6Qi
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 @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 @MarkHMichalski: Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma re…
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 @FAndreMD: This paper has an altimetric score >1000 <2 weeks after release. certainly one of the "must read" in the medical literature i…
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% (±…
Convolutional neural network outperforming dermatologists in melanoma detection. Will CNN become an assistant to dermatologists in their daily work? https://t.co/yHnr8REYUA
RT @ylecun: ConvNet outperforms human dermatologists for melanoma detection. Dermatologists in level-II protocol: - sensitivity: 88.9% (±…
Convolutional #NeuralNetworks 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/uBV6hK2UWd
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 systematically. See whole thre
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: 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% (±…
#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/gEDwqrfoPS
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 @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 @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% (±…
A must lool #DataScience #DeepLearning https://t.co/3QvoPGNmRh
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% (±…
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/tesTX9ZxZS
RT @ylecun: ConvNet outperforms human dermatologists for melanoma detection. Dermatologists in level-II protocol: - sensitivity: 88.9% (±…