33 citations · 140 across the 19 of their papers we have counts for
6 papers · 1 filter
Biomedical image analysis competitions: The state of current participation practice
Matthias Eisenmann, Annika Reinke, Vivienn Weru +352
The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known abou…
Joint Debiased Representation and Image Clustering Learning with Self-Supervision
Shunjie-Fabian Zheng, JaeEun Nam, Emilio Dorigatti +3
Contrastive learning is among the most successful methods for visual representation learning, and its performance can be further improved by jointly performing clustering on the le…
Robust and Efficient Imbalanced Positive-Unlabeled Learning with Self-supervision
Emilio Dorigatti, Jonas Schweisthal, Bernd Bischl +1
Learning from positive and unlabeled (PU) data is a setting where the learner only has access to positive and unlabeled samples while having no information on negative examples. Su…
FiLM-Ensemble: Probabilistic Deep Learning via Feature-wise Linear Modulation
Mehmet Ozgur Turkoglu, Alexander Becker, Hüseyin Anil Gündüz +6
The ability to estimate epistemic uncertainty is often crucial when deploying machine learning in the real world, but modern methods often produce overconfident, uncalibrated uncer…
Analyzing the Effects of Handling Data Imbalance on Learned Features from Medical Images by Looking Into the Models
Ashkan Khakzar, Yawei Li, Yang Zhang +5
One challenging property lurking in medical datasets is the imbalanced data distribution, where the frequency of the samples between the different classes is not balanced. Training…
Uncertainty-aware Pseudo-label Selection for Positive-Unlabeled Learning
Emilio Dorigatti, Jann Goschenhofer, Benjamin Schubert +2
Positive-unlabeled learning (PUL) aims at learning a binary classifier from only positive and unlabeled training data. Even though real-world applications often involve imbalanced…