12 papers
Tumor-aware augmentation with task-guided attention analysis improves rectal cancer segmentation from magnetic resonance images
Aneesh Rangnekar, Joao Miranda, Natally Horvat +13
Although self-supervised pretraining is expected to learn broadly transferable representations, its effectiveness across imaging modalities substantially different from the pretrai…
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…
Prediction of Rectal Cancer Regrowth from Longitudinal Endoscopy
Jorge Tapias Gomez, Despoina Kanata, Aneesh Rangnekar +8
Clinical trial studies indicate benefit of watch-and-wait (WW) surveillance for patients with rectal cancer showing a complete or near clinical response (CR) directly after treatme…
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…