Agree that plain results tables are often not very meaningful?
🆕🆕🆕Then check out our "Beyond rankings" #MedIA paper: https://t.co/htsJIasLyE by @TobiRoss11@annika_reinke1 et al.
We address the question which properties of an image characterize failure cases. 1/2
Are you excited about both, advanced ML methods and healthcare applications? Then this Postdoc position might be a great fit for you. In collaboration with Ulli Köthe‘s lab, we will work on uncertainty-aware image analysis with invertible neural networks. @MiccaiStudents
🪙🪙🪙This is the paper I was waiting for:
https://t.co/sYrkr71DZK
In full support of my hypothesis that most ML papers in healthcare are not very useful.
Thanks @random_walker and @sayashk as well as @michaelhoffman for the catch!
Highly relevant @bias_sig@MICCAI_Society
An important detail in pose optimization & bundle adjustment problems: each correspondence pair has 3 residuals (2 for re-projection errors)!
Merging these into a single residual collapses rows of the underlying Jaccobi matrix and 2nd-order / quasi Newton solvers won't work.
How to achieve patient benefit with #SurgicalDataScience?
Opinion of experts from 51 institutions based on multi-stage Delphi process. Just accepted by Medical Image Analysis https://t.co/NGQgvT802H with many contributors from @IPCAI_conf @ellislifehd and @MICCAI_society (1/3)
Can you detect COVID-19 using Machine Learning? 🤔
You have an X-ray or CT scan and the task is to detect if the patient has COVID-19 or not. Sounds doable, right?
None of the 415 ML papers published on the subject in 2020 was usable. Not a single one!
Let's see why 👇
@derspiegel Dieser Artikel, der ohne ersichtliche Notwendigkeit über meine hochgeschätzte Kollegin @MinuTizabi so abschätzig und arrogant über ihr Leben sich herausnimmt (falsch) zu urteilen, ist absolutes Bildniveau. Pfui @derspiegel!
Do Vision Transformers See Like Convolutional Neural Networks?
New paper https://t.co/mxLCIRBRLy
The successes of Transformers in computer vision prompts a fundamental question: how are they solving these tasks? Do Transformers act like CNNs, or learn very different features?
Takeaway from @karpathy's CVPR talk:
The most successful ML projects in prod (Tesla, iPhone, Amazon drones, Zipline) are where you own the entire stack.
They iterate not just ML algorithms but also:
- how to collect/label data
- infrastructure
- hardware ML models run on
I keep seeing the line 'EU is having vaccine problems because it was too slow in negotiating contracts' repeated in 🇬🇧&🇺🇸 media.
I want to push back on this narrative because I think it's missing where real EU-level mistakes lie. Let's review what happened in past year (��1/17)