259 citations · 492 across the 28 of their papers we have counts for
11 papers · 1 filter
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…
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…
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…
Segmentation Regularized Training for Multi-Domain Deep Learning Registration applied to MR-Guided Prostate Cancer Radiotherapy
Sudharsan Madhavan, Chengcheng Gui, Lando Bosma +8
Background: Accurate deformable image registration (DIR) is required for contour propagation and dose accumulation in MR-guided adaptive radiotherapy (MRgART). This study trained a…
Modality-agnostic, patient-specific digital twins modeling temporally varying digestive motion
Jorge Tapias Gomez, Nishant Nadkarni, Lando S. Bosma +7
Objective: Clinical implementation of deformable image registration (DIR) requires voxel-based spatial accuracy metrics such as manually identified landmarks, which are challenging…
Self-distilled Masked Attention guided masked image modeling with noise Regularized Teacher (SMART) for medical image analysis
Jue Jiang, Aneesh Rangnekar, Chloe Min Seo Choi +1
Pretraining vision transformers (ViT) with attention guided masked image modeling (MIM) has shown to increase downstream accuracy for natural image analysis. Hierarchical shifted w…
Nested-block self-attention for robust radiotherapy planning segmentation
Harini Veeraraghavan, Jue Jiang, Sharif Elguindi +5
Although deep convolutional networks have been widely studied for head and neck (HN) organs at risk (OAR) segmentation, their use for routine clinical treatment planning is limited…
Local block-wise self attention for normal organ segmentation
Jue Jiang, Elguindi Sharif, Hyemin Um +2
We developed a new and computationally simple local block-wise self attention based normal structures segmentation approach applied to head and neck computed tomography (CT) images…
Comparison of Patch-Based Conditional Generative Adversarial Neural Net Models with Emphasis on Model Robustness for Use in Head and Neck Cases for MR-Only planning
Peter Klages, Ilyes Benslimane, Sadegh Riyahi +5
A total of twenty paired CT and MR images were used in this study to investigate two conditional generative adversarial networks, Pix2Pix, and Cycle GAN, for generating synthetic C…
Cross-modality (CT-MRI) prior augmented deep learning for robust lung tumor segmentation from small MR datasets
Jue Jiang, Yu-Chi Hu, Neelam Tyagi +4
Lack of large expert annotated MR datasets makes training deep learning models difficult. Therefore, a cross-modality (MR-CT) deep learning segmentation approach that augments trai…
GBM Volumetry using the 3D Slicer Medical Image Computing Platform
Jan Egger, Tina Kapur, Andriy Fedorov +7
Volumetric change in glioblastoma multiforme (GBM) over time is a critical factor in treatment decisions. Typically, the tumor volume is computed on a slice-by-slice basis using MR…