53 citations · 88 across the 16 of their papers we have counts for
4 papers · 2 filters
Improving ovarian cancer segmentation accuracy with transformers through AI-guided labeling
Aneesh Rangnekar, Kevin M. Boehm, Emily A. Aherne +8
Transformer models have demonstrated the capability to produce highly accurate segmentation of organs and tumors. However, model training requires high-quality curated datasets to…
Self-supervised learning improves robustness of deep learning lung tumor segmentation to CT imaging differences
Jue Jiang, Aneesh Rangnekar, Harini Veeraraghavan
Self-supervised learning (SSL) is an approach to extract useful feature representations from unlabeled data, and enable fine-tuning on downstream tasks with limited labeled example…
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…