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DICOMAnon helps imaging teams anonymize, batch process, and automate DICOM workflows without writing custom scripts.
Author profile
Yasin Abdulkadir
Department of Radiation Oncology, University of California, Los Angeles
Automated Dose-Based Anatomic Region Classification of Radiotherapy Treatment for Big Data Applications
Aggregating large radiotherapy datasets for predictive modeling is impeded by inconsistent nomenclature. To address this, we developed a fully automated, metadata-independent framework to classify treatment plans into six Anatomic Regions and identify specifi...
Poster Program · Therapy Physics
Generalizability and Domain Adaptation of a Deep Learning Dose Prediction Model for Head-and-Neck Radiotherapy
Deep learning–based dose prediction models have demonstrated high accuracy in multi-institutional challenges; however, their performance under real-world institutional domain shift remains unclear. This study evaluates the generalizability of a state-of-the-a...
Poster Program · Therapy Physics
Head and Neck Gross Tumor Volume Deep-Learning Autocontouring Model Using Bigroc Collaborative Data
Accurate delineation of Head and Neck (H&N) Gross Tumor Volumes (GTV) is a prerequisite for effective radiotherapy, represents a significant cognitive challenge, and may contribute to an observed outcomes deficit between high-volume and low-volume H&N radioth...
Poster Program · Therapy Physics
Rosamllib: An Open-Source Software Package for Large-Scale Radiotherapy Dicom Ingestion, Indexing, Visualization and Preprocessing
Large-scale radiotherapy research increasingly relies on heterogeneous DICOM datasets containing complex cross-references across imaging and radiotherapy objects. Beyond ingestion, downstream preprocessing for analysis and machine learning requires consistent...
Poster Program · Therapy Physics