A Closer Look at Domain Shift for Deep Learning in Histopathology. - Karin Stacke; Gabriel Eilertsen; Jonas Unger; Claes Lundström (CoRR, 2019)

Evaluation of Contrastive Predictive Coding for Histopathology Applications. - Karin Stacke; Claes Lundström; Gabriel Eilertsen (ML4H@NeurIPS, 2020)

A Study of Deep Learning Colon Cancer Detection in Limited Data Access Scenarios. - Apostolia Tsirikoglou; Karin Stacke; Gabriel Eilertsen; Martin Lindvall; Jonas Unger (CoRR, 2020)

Primary Tumor and Inter-Organ Augmentations for Supervised Lymph Node Colon Adenocarcinoma Metastasis Detection. - Apostolia Tsirikoglou; Karin Stacke; Gabriel Eilertsen; Jonas Unger (MICCAI (5), 2021)

Combatting out-of-distribution errors using model-agnostic meta-learning for digital pathology. - Freja Fagerblom; Karin Stacke; Jesper Molin (Digital Pathology, 2021)

Learning Representations with Contrastive Self-Supervised Learning for Histopathology Applications. - Karin Stacke; Jonas Unger; Claes Lundström; Gabriel Eilertsen (CoRR, 2021)

Proactive Construction of an Annotated Imaging Database for Artificial Intelligence Training. - Caroline Bivik Stadler; Martin Lindvall; Claes Lundström; Anna Bodén; Karin Lindman; Jeronimo Rose; Darren Treanor; Johan Blomma; Karin Stacke; Nicolas Pinchaud; Martin Hedlund; Filip Landgren; et al. (J. Digit. Imaging, 2021)

Measuring Domain Shift for Deep Learning in Histopathology. - Karin Stacke; Gabriel Eilertsen; Jonas Unger; Claes Lundström (IEEE J. Biomed. Health Informatics, 2021)

Correlated Feature Aggregation by Region Helps Distinguish Aggressive from Indolent Clear Cell Renal Cell Carcinoma Subtypes on CT. - Karin Stacke; Indrani Bhattacharya; Justin R. Tse; James D. Brooks; Geoffrey A. Sonn; Mirabela Rusu (CoRR, 2022)

Deep Learning for Digital Pathology in Limited Data Scenarios. - Karin Stacke (2022)