134 citations · 142 across the 6 of their papers we have counts for
6 papers
Fully transformer-based biomarker prediction from colorectal cancer histology: a large-scale multicentric study
Sophia J. Wagner, Daniel Reisenbüchler, Nicholas P. West +28
Background: Deep learning (DL) can extract predictive and prognostic biomarkers from routine pathology slides in colorectal cancer. For example, a DL test for the diagnosis of micr…
Approaching Peak Ground Truth
Florian Kofler, Johannes Wahle, Ivan Ezhov +15
Machine learning models are typically evaluated by computing similarity with reference annotations and trained by maximizing similarity with such. Especially in the biomedical doma…
Local Attention Graph-based Transformer for Multi-target Genetic Alteration Prediction
Daniel Reisenbüchler, Sophia J. Wagner, Melanie Boxberg +1
Classical multiple instance learning (MIL) methods are often based on the identical and independent distributed assumption between instances, hence neglecting the potentially rich…
S5CL: Unifying Fully-Supervised, Self-Supervised, and Semi-Supervised Learning Through Hierarchical Contrastive Learning
Manuel Tran, Sophia J. Wagner, Melanie Boxberg +1
In computational pathology, we often face a scarcity of annotations and a large amount of unlabeled data. One method for dealing with this is semi-supervised learning which is comm…
Hard Sample Aware Noise Robust Learning for Histopathology Image Classification
Chuang Zhu, Wenkai Chen, Ting Peng +2
Deep learning-based histopathology image classification is a key technique to help physicians in improving the accuracy and promptness of cancer diagnosis. However, the noisy label…
Multi-level colonoscopy malignant tissue detection with adversarial CAC-UNet
Chuang Zhu, Ke Mei, Ting Peng +4
The automatic and objective medical diagnostic model can be valuable to achieve early cancer detection, and thus reducing the mortality rate. In this paper, we propose a highly eff…