Grappling with the Black Box: Semi-supervised and Unsupervised Learning in Medical Imaging AI #RSNA21 session R4-CIN25 today 12/2 at 1:30PM CST with @KMagudia moderating and presenting along with George Shih, @quantrad & @DrLindaMoy #AI #MachineLearning https://t.co/JSdM6pZlCy
A low resolution photo of punky-looking Zombie that has 3 attributes, a Black Lipstick, a Frown, and a Medical Mask #Text2Cryptopunks #NFTs #NFTcommuity #cryptopunks #aiart #generativeart #MachineLearning https://t.co/Sl1VG6rpHJ
Do we need to re-think #MachineLearning for (brain) medicine? Current proof-of-concept studies on retrospective, repository data may not yield a relevant practical impact @den_hed & I argue for more "doctors at the black box" for more user perspective https://www.nature.com/articles/s41380-020-00931-z https://t.co/6NJrne9tuu
Unbox the Black-box for the Medical Explainable AI via Multi-modal and Multi-centre Data Fusion: A Mini-Review, Two Showcases and Beyond https://deepai.org/publication/unbox-the-black-box-for-the-medical-explainable-ai-via-multi-modal-and-multi-centre-data-fusion-a-mini-review-two-showcases-and-beyond by Guang Yang et al. #MachineLearning #NeuralNetwork
Unpacking the Black Box in Artificial Intelligence for Medicine - Undark Magazine Read more here: https://ift.tt/2P9gpSt #ArtificialIntelligence #AI #DataScience #MachineLearning #BigData #DeepLearning #NLP #Robots #IoT
Black box models are not acceptable for high stakes decisions in medicine. Models must be interpretable, not just explainable (explanations are wrong by definition). Here’s your great long-read for today by @CynthiaRudin https://arxiv.org/abs/1811.10154 #rstats #DataScience
