331 citations · 348 across the 5 of their papers we have counts for
5 papers
MEAL: Manifold Embedding-based Active Learning
Deepthi Sreenivasaiah, Johannes Otterbach, Thomas Wollmann
Image segmentation is a common and challenging task in autonomous driving. Availability of sufficient pixel-level annotations for the training data is a hurdle. Active learning hel…
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…
Chameleon: A Semi-AutoML framework targeting quick and scalable development and deployment of production-ready ML systems for SMEs
Johannes Otterbach, Thomas Wollmann
Developing, scaling, and deploying modern Machine Learning solutions remains challenging for small- and middle-sized enterprises (SMEs). This is due to a high entry barrier of buil…
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…