Publications
- Segmentation of Non-Small Cell Lung Carcinomas: Introducing DRU-Net and Multi-Lens Distortion Read the publication
- Regional Image Quality Scoring for 2-D Echocardiography Using Deep Learning
- Immunohistochemistry guided segmentation of benign epithelial cells, in situ lesions, and invasive epithelial cells in breast cancer slides
- Lightweight image segmentation for echocardiography
- Predicting estrogen receptor status from HE-stained breast cancer slides using artificial intelligence
- Generative augmentations for improved cardiac ultrasound segmentation using diffusion models Read the publication
- Real-time deep learning-based image guiding and automated left ventricular measurements to reduce test-retest variability Read the publication
- Novel automated quality indicators for echocardiography and their importance for left ventricular strain
- Deep learning in echocardiography: real-time measurements of left ventricular wall thickness and chamber dimensions in the parasternal long-axis view
- Deep learning in echocardiography: Fully automated B-mode MAPSE measurements in real-time
Other
- Deep learning-based glass removal in whole slide images
- Reliable deep learning for echocardiography image analysis
- The effect of deep learning guidance and automated measurements on the reproducibility of left ventricular ejection fraction and global longitudinal strain in breast cancer patients receiving trastuzumab
- Regional quality estimation for echocardiography using deep LEARNING
- Regional quality estimation for echocardiography using deep learning
- Real-time segmentation of the aorta in 2D ultrasound images for detecting abdominal aorta aneurism - abstract
- Forbedret diagnostikk av lungekreft: Kunstig intelligens ved endobronkial ultralyd
- Response to “Minimal Detectable Change and Reproducibility of Echocardiographic Strain: Implications for Clinical Practice”
- Deep learning in real-time ultrasound and digital pathology - From annotation and training to real-world application
- FAST: A framework for high-performance medical image computing and visualization