@INM7_ISN 2 years ago
Postdoc position available @HHU_de & @fz_juelich We are looking for a new colleague to work on an interdisciplinary project - #DataScience & #MachineLearning on medical data - Strong team with excellent infrastructure - Duration of the position on to 4 years ! https://t.co/PTUMPk2398
@INM7_ISN 2 years ago
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
@HumanBrainProj 3 years 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 3 years 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 4 years 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 4 years 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 4 years 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.