53 citations · 88 across the 16 of their papers we have counts for
22 papers
Adaptive Computation with Conformal Certification
Shehtab Zaman, Aneesh Rangnekar, Nancy Lee +1
Scientific imaging data involving medical (e.g., 3D radiographic, microscopic images) and astronomical sensors pose different challenges from natural images. One key challenge is t…
Parameter-Efficient pretrained-CT-to-MRI Transfer for Rectal Cancer Segmentation: Performance-Calibration Trade-offs
Aneesh Rangnekar, Jorge Tapias Gomez, Joseph O Deasy +1
Accurate rectal cancer segmentation from magnetic resonance imaging (MRI) is essential for adaptive radiotherapy and tumor response assessment, but deployment also requires computa…
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-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…
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…
MHub.ai: A Simple, Standardized, and Reproducible Platform for AI Models in Medical Imaging
Leonard Nürnberg, Dennis Bontempi, Suraj Pai +17
Artificial intelligence (AI) has the potential to transform medical imaging by automating image analysis and accelerating clinical research. However, research and clinical use are…