Search Results for author:
Found 6 papers, 0 papers with code
Analysis of clinical, dosimetric and radiomic features for predicting local failure after stereotactic radiotherapy of brain metastases in malignant melanoma
Background: The aim of this study was to investigate the role of clinical, dosimetric and pretherapeutic magnetic resonance imaging (MRI) features for lesion-specific outcome prediction of stereotactic radiotherapy (SRT) in patients with brain metastases from malignant melanoma (MBM).
All Sizes Matter: Improving Volumetric Brain Segmentation on Small Lesions
Our experiments demonstrate the utility of the ad ditional blob loss and the subtraction sequence.
A unified 3D framework for Organs at Risk Localization and Segmentation for Radiation Therapy Planning
Automatic localization and segmentation of organs-at-risk (OAR) in CT are essential pre-processing steps in medical image analysis tasks, such as radiation therapy planning.
Evaluating the Robustness of Self-Supervised Learning in Medical Imaging
Self-supervision has demonstrated to be an effective learning strategy when training target tasks on small annotated data-sets.
Deep Learning Based HPV Status Prediction for Oropharyngeal Cancer Patients
We investigated the ability of deep learning models for imaging based HPV status detection.
Deep Reinforcement Learning for Organ Localization in CT
Robust localization of organs in computed tomography scans is a constant pre-processing requirement for organ-specific image retrieval, radiotherapy planning, and interventional image analysis.
