Search Results for author: Jingna Qiu

Found 6 papers, 3 papers with code

Style-Extracting Diffusion Models for Semi-Supervised Histopathology Segmentation

no code implementations21 Mar 2024 Mathias Öttl, Frauke Wilm, Jana Steenpass, Jingna Qiu, Matthias Rübner, Arndt Hartmann, Matthias Beckmann, Peter Fasching, Andreas Maier, Ramona Erber, Bernhard Kainz, Katharina Breininger

Specifically, we utilize 1) a style conditioning mechanism which allows to inject style information of previously unseen images during image generation and 2) a content conditioning which can be targeted to a downstream task, e. g., layout for segmentation.

Image Generation Segmentation

Adaptive Region Selection for Active Learning in Whole Slide Image Semantic Segmentation

1 code implementation14 Jul 2023 Jingna Qiu, Frauke Wilm, Mathias Öttl, Maja Schlereth, Chang Liu, Tobias Heimann, Marc Aubreville, Katharina Breininger

We find that the efficiency of this method highly depends on the choice of AL step size (i. e., the combination of region size and the number of selected regions per WSI), and a suboptimal AL step size can result in redundant annotation requests or inflated computation costs.

Active Learning Informativeness +2

Multi-Scanner Canine Cutaneous Squamous Cell Carcinoma Histopathology Dataset

1 code implementation11 Jan 2023 Frauke Wilm, Marco Fragoso, Christof A. Bertram, Nikolas Stathonikos, Mathias Öttl, Jingna Qiu, Robert Klopfleisch, Andreas Maier, Katharina Breininger, Marc Aubreville

Additionally, to quantify the inherent scanner-induced domain shift, we train a tumor segmentation network on each scanner subset and evaluate the performance both in- and cross-domain.

Domain Generalization Tumor Segmentation

Improved HER2 Tumor Segmentation with Subtype Balancing using Deep Generative Networks

no code implementations11 Nov 2022 Mathias Öttl, Jana Mönius, Matthias Rübner, Carol I. Geppert, Jingna Qiu, Frauke Wilm, Arndt Hartmann, Matthias W. Beckmann, Peter A. Fasching, Andreas Maier, Ramona Erber, Katharina Breininger

We show the suitability of Generative Adversarial Networks (GANs) and especially diffusion models to create realistic images based on subtype-conditioning for the use case of HER2-stained histopathology.

Segmentation Tumor Segmentation

Pan-tumor CAnine cuTaneous Cancer Histology (CATCH) dataset

1 code implementation27 Jan 2022 Frauke Wilm, Marco Fragoso, Christian Marzahl, Jingna Qiu, Chloé Puget, Laura Diehl, Christof A. Bertram, Robert Klopfleisch, Andreas Maier, Katharina Breininger, Marc Aubreville

Due to morphological similarities, the differentiation of histologic sections of cutaneous tumors into individual subtypes can be challenging.

whole slide images

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