Journal
2019 IEEE 16TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2019)
Volume -, Issue -, Pages 1869-1872Publisher
IEEE
Keywords
Machine learning; Histopathology imaging
Funding
- European Regional Development Fund
- Walloon Region [Wallonia-Biomed grant] [411132-957270]
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Digital pathology produces a lot of images For machine learning applications, these images need to be annotated, which can be complex and time consuming Therefore, outside of a few benchmark datasets, real-world applications often rely on data with scarce or unreliable annotations. In this paper, we quantitatively analyze how different types of perturbations influence the results of a typical deep learning algorithm by artificially weakening the annotations of a benchmark biomedical dataset. We use classical machine learning paradigms (semi-supervised, noisy and weak learning) adapted to deep learning to try to counteract those effects, and analyze the effectiveness of these methods in addressing different types of weakness.
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