13 papers
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…
Dual Cross-Attention Siamese Transformer for Rectal Tumor Regrowth Assessment in Watch-and-Wait Endoscopy
Jorge Tapias Gomez, Despoina Kanata, Aneesh Rangnekar +4
Increasing evidence supports watch-and-wait (WW) surveillance for patients with rectal cancer who show clinical complete response (cCR) at restaging following total neoadjuvant tre…
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…
Co-distilled attention guided masked image modeling with noisy teacher for self-supervised learning on medical images
Jue Jiang, Aneesh Rangnekar, Harini Veeraraghavan
Masked image modeling (MIM) is a highly effective self-supervised learning (SSL) approach to extract useful feature representations from unannotated data. Predominantly used random…
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.…