Prognostic prediction of histopathological images by local binary patterns and RUSBoost. - Jarle Urdal; Kjersti Engan; Vebjom Kvikstad; Emilius A. M. Janssen (EUSIPCO, 2017)

Multiclass Tissue Classification of Whole-Slide Histological Images using Convolutional Neural Networks. - Rune Wetteland; Kjersti Engan; Trygve Eftestøl; Vebjørn Kvikstad; Emilius A. M. Janssen (ICPRAM, 2019)

Semi-supervised Tissue Segmentation of Histological Images. - Ove Nicolai Dalheim; Rune Wetteland; Vebjørn Kvikstad; Emiel A. M. Janssen; Kjersti Engan (CVCS, 2020)

Spatial Attention-Based Deep Learning System for Breast Cancer Pathological Complete Response Prediction with Serial Histopathology Images in Multiple Stains. - Hongyi Duanmu; Shristi Bhattarai; Hongxiao Li; Chia Cheng Cheng; Fusheng Wang 001; George Teodoro; Emiel A. M. Janssen; Keerthi Gogineni; Preeti Subhedar; Ritu Aneja; Jun Kong 002 (MICCAI (8), 2021)

Automatic Diagnostic Tool for Predicting Cancer Grade in Bladder Cancer Patients Using Deep Learning. - Rune Wetteland; Vebjørn Kvikstad; Trygve Eftestøl; Erlend Tøssebro; Melinda Lillesand; Emiel A. M. Janssen; Kjersti Engan (IEEE Access, 2021)

miRNA normalization enables joint analysis of several datasets to increase sensitivity and to reveal novel miRNAs differentially expressed in breast cancer. - Shay Ben-Elazar; Miriam Ragle Aure; Kristin Jonsdottir; Suvi-Katri Leivonen; Vessela N. Kristensen; Emiel A. M. Janssen; Kristine Kleivi Sahlberg; Ole Christian Lingjærde; Zohar Yakhini (PLoS Comput. Biol., 2021)

Challenging Mitosis Detection Algorithms: Global Labels Allow Centroid Localization. - Claudio Fernandez-Martín; Umay Kiraz; Julio Silva-Rodríguez; Sandra Morales; Emiel A. M. Janssen; Valery Naranjo (IDEAL, 2022)

Invasive Cancerous Area Detection in Non-Muscle Invasive Bladder Cancer Whole Slide Images. - Saul Fuster; Farbod Khoraminia; Umay Kiraz; Neel Kanwal; Vebjørn Kvikstad; Trygve Eftestøl; Tahlita C. M. Zuiverloon; Emiel A. M. Janssen; Kjersti Engan (IVMSP, 2022)

A spatial attention guided deep learning system for prediction of pathological complete response using breast cancer histopathology images. - Hongyi Duanmu; Shristi Bhattarai; Hongxiao Li; Zhan Shi; Fusheng Wang 001; George Teodoro; Keerthi Gogineni; Preeti Subhedar; Umay Kiraz; Emiel A. M. Janssen; Ritu Aneja; Jun Kong 002 (Bioinform., 2022)

Detection and Localization of Melanoma Skin Cancer in Histopathological Whole Slide Images. - Neel Kanwal; Roger Amundsen; Helga Hardardottir; Luca Tomasetti; Erling Sandøy Undersrud; Emiel A. M. Janssen; Kjersti Engan (EUSIPCO, 2023)

Deep Learning for Predicting Metastasis on Melanoma Wsis. - Christopher Andreassen; Saul Fuster; Helga Hardardottir; Emiel A. M. Janssen; Kjersti Engan (ISBI, 2023)

Physical Color Calibration of Digital Pathology Scanners for Robust Artificial Intelligence Assisted Cancer Diagnosis. - Xiaoyi Ji; Richard Salmon; Nita Mulliqi; Umair Khan; Yinxi Wang; Anders Blilie; Henrik Olsson; Bodil Ginnerup Pedersen; Karina Dalsgaard Sørensen; Benedicte Parm Ulhøi; Svein Reidar Kjosavik; Emilius A. M. Janssen; et al. (CoRR, 2023)

Balancing Privacy and Progress in Artificial Intelligence: Anonymization in Histopathology for Biomedical Research and Education. - Neel Kanwal; Emiel A. M. Janssen; Kjersti Engan (CoRR, 2023)

A Dual Convolutional Neural Network Pipeline for Melanoma Diagnostics and Prognostics. - Marie Bø-Sande; Edvin Benjaminsen; Neel Kanwal; Saul Fuster; Helga Hardardottir; Ingrid Lundal; Emilius A. M. Janssen; Kjersti Engan (NLDL, 2024)

Uninformed Teacher-Student for hard-samples distillation in weakly supervised mitosis localization. - Claudio Fernandez-Martín; Julio Silva-Rodríguez; Umay Kiraz; Sandra Morales; Emiel A. M. Janssen; Valery Naranjo (Comput. Medical Imaging Graph., 2024)

Self-Contrastive Weakly Supervised Learning Framework for Prognostic Prediction Using Whole Slide Images. - Saul Fuster; Farbod Khoraminia; Julio Silva-Rodríguez; Umay Kiraz; Geert J. L. H. van Leenders; Trygve Eftestøl; Valery Naranjo; Emiel A. M. Janssen; Tahlita C. M. Zuiverloon; Kjersti Engan (CoRR, 2024)

NMGrad: Advancing Histopathological Bladder Cancer Grading with Weakly Supervised Deep Learning. - Saul Fuster; Umay Kiraz; Trygve Eftestøl; Emiel A. M. Janssen; Kjersti Engan (CoRR, 2024)

Equipping computational pathology systems with artifact processing pipelines: a showcase for computation and performance trade-offs. - Neel Kanwal; Farbod Khoraminia; Umay Kiraz; Andrés Mosquera-Zamudio; Carlos Monteagudo; Emiel A. M. Janssen; Tahlita C. M. Zuiverloon; Chunming Rong; Kjersti Engan (BMC Medical Informatics Decis. Mak., 2024)

Foundation Models - A Panacea for Artificial Intelligence in Pathology? - Nita Mulliqi; Anders Blilie; Xiaoyi Ji; Kelvin Szolnoky; Henrik Olsson; Sol Erika Boman; Matteo Titus; Geraldine Martinez Gonzalez; Julia Anna Mielcarz; Masi Valkonen; Einar Gudlaugsson; Svein Reidar Kjosavik; et al. (CoRR, 2025)

The impact of tissue detection on diagnostic artificial intelligence algorithms in digital pathology. - Sol Erika Boman; Nita Mulliqi; Anders Blilie; Xiaoyi Ji; Kelvin Szolnoky; Einar Gudlaugsson; Emiel A. M. Janssen; Svein Reidar Kjosavik; José Asenjo; Marcello Gambacorta; Paolo Libretti; Marcin Braun; et al. (CoRR, 2025)

Artificial Intelligence-Assisted Prostate Cancer Diagnosis for Reduced Use of Immunohistochemistry. - Anders Blilie; Nita Mulliqi; Xiaoyi Ji; Kelvin Szolnoky; Sol Erika Boman; Matteo Titus; Geraldine Martinez Gonzalez; José Asenjo; Marcello Gambacorta; Paolo Libretti; Einar Gudlaugsson; Svein Reidar Kjosavik; et al. (CoRR, 2025)