@HumanBrainProj 6 months ago
Do you want to learn about: ◦ Understanding brain organization? ◦ #MachineLearning for individual #prediction, #clinical translation & personalized #medicine? Then follow HBP Scientist Prof. Dr. Simon Eickhoff at @INM7_ISN! https://t.co/eNHn1WKe72
@HumanBrainProj 6 months ago
Prof. Dr. Simon Eickhoff's work focuses on: ◦ Understanding brain organization ◦ #MachineLearning for individual prediction, #clinical translation & personalized #medicine All of which is highly relevant to the mission of the Human Brain Project. Follow him at @INM7_ISN https://t.co/vZe55zoiuU
@INM7_ISN 11 months ago
What's your evidence? Here Bert Heinrichs, a philosopher, and I outline some thoughts on discursive practice, acceptance and responsibility in the context of #MachineLearning for medical diagnosis & prediction https://onlinelibrary.wiley.com/doi/full/10.1002/hbm.24886 @KordingLab @DrHughHarvey @NewMindMirror https://t.co/zwcQdl5jLb
@INM7_ISN 1 year ago
Eysenk's astonishing work on "cancer personality". If results are too good to be true, there is usually a good reason for this Highly relevant to current work on #MachineLearning in neuroimaging and Medicine. Real life is too complex for >>90% accuracy https://cosmosmagazine.com/society/is-this-one-of-the-worst-scientific-scandals-of-all-time
@INM7_ISN 1 year ago
The great irony of #MachineLearning for medical applications: On one hand, everything is "data-driven", "makes no assumptions" etc. On the other, diagnostic labels are taken as ground truth.