Area of research
Oncology · Cancer Research
Research interest
Research interests include Breast Cancer Treatment Studies, Radiomics and Machine Learning in Medical Imaging, AI in cancer detection, and Cancer Immunotherapy and Biomarkers.
Pathologist-Read vs AI-Driven Assessment of Tumor-Infiltrating Lymphocytes in Melanoma
Real-world overall survival and characteristics of patients with ER-zero and ER-low HER2-negative breast cancer treated as triple-negative breast cancer: a Swedish population-based cohort study
Image‐based multiplex immune profiling of cancer tissues: translational implications. A report of the International Immuno‐oncology Biomarker Working Group on Breast Cancer
Ensemble-based deep learning improves detection of invasive breast cancer in routine histopathology images
The analytical and clinical validity of AI algorithms to score TILs in TNBC: can we use different machine learning models interchangeably?
Pitfalls in machine learning‐based assessment of tumor‐infiltrating lymphocytes in breast cancer: A report of the International Immuno‐Oncology Biomarker Working Group on Breast Cancer
Spatial analyses of immune cell infiltration in cancer: current methods and future directions: A report of the International Immuno‐Oncology Biomarker Working Group on Breast Cancer
Systematically higher Ki67 scores on core biopsy samples compared to corresponding resection specimen in breast cancer: a multi-operator and multi-institutional study
Survival Outcomes, Digital TILs, and On-treatment PET/CT During Neoadjuvant Therapy for HER2-positive Breast Cancer: Results from the Randomized PREDIX HER2 Trial
Objective assessment of tumor infiltrating lymphocytes as a prognostic marker in melanoma using machine learning algorithms
Improved breast cancer histological grading using deep learning
Predicting Molecular Phenotypes from Histopathology Images: A Transcriptome-Wide Expression–Morphology Analysis in Breast Cancer
Variability in Breast Cancer Biomarker Assessment and the Effect on Oncological Treatment Decisions: A Nationwide 5-Year Population-Based Study
An Open-Source, Automated Tumor-Infiltrating Lymphocyte Algorithm for Prognosis in Triple-Negative Breast Cancer
Automated digital TIL analysis (ADTA) adds prognostic value to standard assessment of depth and ulceration in primary melanoma
Assessment of Ki67 in Breast Cancer: Updated Recommendations From the International Ki67 in Breast Cancer Working Group
Artificial intelligence as the next step towards precision pathology
Report on computational assessment of Tumor Infiltrating Lymphocytes from the International Immuno-Oncology Biomarker Working Group
Application of a risk-management framework for integration of stromal tumor-infiltrating lymphocytes in clinical trials
Deep Learning Based on Standard H&E Images of Primary Melanoma Tumors Identifies Patients at Risk for Visceral Recurrence and Death
An open source automated tumor infiltrating lymphocyte algorithm for prognosis in melanoma
Quantitative assessment of PD-L1 as an analyte in immunohistochemistry diagnostic assays using a standardized cell line tissue microarray