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Tumor-aware augmentation with task-guided attention analysis improves rectal cancer segmentation from magnetic resonance images
Aneesh Rangnekar, Joao Miranda, Natally Horvat +14
Although self-supervised pretraining is expected to learn broadly transferable representations, its effectiveness across imaging modalities substantially different from the pretrai…
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…
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…
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.…
Swin transformers are robust to distribution and concept drift in endoscopy-based longitudinal rectal cancer assessment
Jorge Tapias Gomez, Aneesh Rangnekar, Hannah Williams +4
Endoscopic images are used at various stages of rectal cancer treatment starting from cancer screening, diagnosis, during treatment to assess response and toxicity from treatments…
Quantifying uncertainty in lung cancer segmentation with foundation models applied to mixed-domain datasets
Aneesh Rangnekar, Nishant Nadkarni, Jue Jiang +1
Medical image foundation models have shown the ability to segment organs and tumors with minimal fine-tuning. These models are typically evaluated on task-specific in-distribution…