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Unsupervised pre-training helps to conserve views from input distribution
In case of binary features, we show that conditional independence allows to extract label's information with a linear model and therefore helps to solve under-fitting.
Information theoretic learning of robust deep representations
We propose a novel objective function for learning robust deep representations of data based on information theory.
Weakly supervised training of pixel resolution segmentation models on whole slide images
We present a novel approach to train pixel resolution segmentation models on whole slide images in a weakly supervised setup.
Segmenting Potentially Cancerous Areas in Prostate Biopsies using Semi-Automatically Annotated Data
The presence of basal cells is the most accepted biomarker for benign glandular tissue and the absence of basal cells is a strong indicator of acinar prostatic adenocarcinoma, the most common form of prostate cancer.
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