259 citations · 492 across the 28 of their papers we have counts for
32 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…
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…
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…
brat: Aligned Multi-View Embeddings for Brain MRI Analysis
Maxime Kayser, Maksim Gridnev, Wanting Wang +6
We present brat (brain report alignment transformer), a multi-view representation learning framework for brain magnetic resonance imaging (MRI) trained on MRIs paired with clinical…
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…