8 citations · 9 across the 2 of their papers we have counts for
3 papers · 1 filter
DAAIN: Detection of Anomalous and Adversarial Input using Normalizing Flows
Samuel von Baußnern, Johannes Otterbach, Adrian Loy +2
Despite much recent work, detecting out-of-distribution (OOD) inputs and adversarial attacks (AA) for computer vision models remains a challenge. In this work, we introduce a novel…
Predicting breast tumor proliferation from whole-slide images: the TUPAC16 challenge
Mitko Veta, Yujing J. Heng, Nikolas Stathonikos +30
Tumor proliferation is an important biomarker indicative of the prognosis of breast cancer patients. Assessment of tumor proliferation in a clinical setting is highly subjective an…
Automatic breast cancer grading in lymph nodes using a deep neural network
Thomas Wollmann, Karl Rohr
The progression of breast cancer can be quantified in lymph node whole-slide images (WSIs). We describe a novel method for effectively performing classification of whole-slide imag…