259 citations · 299 across the 16 of their papers we have counts for
16 papers · 1 filter
Enhancing Prostate Cancer Segmentation for Multi-Domain Generalization using a novel Parallel-Route Coherent Mixup Regularization Training
Josiah Simeth, Sudharsan Madhavan, Victoria Brennan +7
MRI guided adaptive radiotherapy (MRgART) for prostate cancer (PCa) targets tumors while sparing organs from unnecessary radiation. Daily treatment adaptation requires accurate seg…
Tumor-anchored deep feature random forests for out-of-distribution detection in lung cancer segmentation
Aneesh Rangnekar, Harini Veeraraghavan
Accurate segmentation of lung tumors from 3D computed tomography (CT) scans is essential for automated treatment planning and response assessment. Despite self-supervised pretraini…
Random forest-based out-of-distribution detection for robust lung cancer segmentation
Aneesh Rangnekar, Harini Veeraraghavan
Accurate detection and segmentation of cancerous lesions from computed tomography (CT) scans is essential for automated treatment planning and cancer treatment response assessment.…
Transformer-based cardiac substructure segmentation from contrast and non-contrast computed tomography for radiotherapy planning
Aneesh Rangnekar, Nikhil Mankuzhy, Jonas Willmann +5
Accurate segmentation of cardiac substructures on computed tomography (CT) scans is essential for radiotherapy planning but typically requires large annotated datasets and often ge…
Improving ovarian cancer segmentation accuracy with transformers through AI-guided labeling
Aneesh Rangnekar, Kevin M. Boehm, Emily A. Aherne +8
Transformer models have demonstrated the capability to produce highly accurate segmentation of organs and tumors. However, model training requires high-quality curated datasets to…
Self-supervised learning improves robustness of deep learning lung tumor segmentation to CT imaging differences
Jue Jiang, Aneesh Rangnekar, Harini Veeraraghavan
Self-supervised learning (SSL) is an approach to extract useful feature representations from unlabeled data, and enable fine-tuning on downstream tasks with limited labeled example…