no code implementations • 29 Sep 2022 • Karin Stacke, Indrani Bhattacharya, Justin R. Tse, James D. Brooks, Geoffrey A. Sonn, Mirabela Rusu
CorrFABR consists of three main steps: (1) Feature Aggregation where region-level features are extracted from radiology and pathology images, (2) Fusion where radiology features correlated with pathology features are learned on a region level, and (3) Prediction where the learned correlated features are used to distinguish aggressive from indolent clear cell RCC using CT alone as input.
1 code implementation • 10 Dec 2021 • Karin Stacke, Jonas Unger, Claes Lundström, Gabriel Eilertsen
We bring forward a number of considerations, such as view generation for the contrastive objective and hyper-parameter tuning.
no code implementations • 17 Sep 2021 • Apostolia Tsirikoglou, Karin Stacke, Gabriel Eilertsen, Jonas Unger
The scarcity of labeled data is a major bottleneck for developing accurate and robust deep learning-based models for histopathology applications.
no code implementations • 20 May 2020 • Apostolia Tsirikoglou, Karin Stacke, Gabriel Eilertsen, Martin Lindvall, Jonas Unger
One such scenario relates to detecting tumor metastasis in lymph node tissue, where the low ratio of tumor to non-tumor cells makes the diagnostic task hard and time-consuming.
1 code implementation • 25 Sep 2019 • Karin Stacke, Gabriel Eilertsen, Jonas Unger, Claes Lundström
Most centrally, we present a novel measure for evaluating the distance between domains in the context of the learned representation of a particular model.