41 citations · 50 across the 11 of their papers we have counts for
12 papers
Novel Radiomic Measurements of Tumor- Associated Vasculature Morphology on Clinical Imaging as a Biomarker of Treatment Response in Multiple Cancers
Nathaniel Braman, Prateek Prasanna, Kaustav Bera +14
Purpose: Tumor-associated vasculature differs from healthy blood vessels by its chaotic architecture and twistedness, which promotes treatment resistance. Measurable differences in…
Temporal Context Matters: Enhancing Single Image Prediction with Disease Progression Representations
Aishik Konwer, Xuan Xu, Joseph Bae +2
Clinical outcome or severity prediction from medical images has largely focused on learning representations from single-timepoint or snapshot scans. It has been shown that disease…
CD-Net: Histopathology Representation Learning using Pyramidal Context-Detail Network
Saarthak Kapse, Srijan Das, Prateek Prasanna
Extracting rich phenotype information, such as cell density and arrangement, from whole slide histology images (WSIs), requires analysis of large field of view, i.e more contexual…
Brain Cancer Survival Prediction on Treatment-na ive MRI using Deep Anchor Attention Learning with Vision Transformer
Xuan Xu, Prateek Prasanna
Image-based brain cancer prediction models, based on radiomics, quantify the radiologic phenotype from magnetic resonance imaging (MRI). However, these features are difficult to re…
Lung Swapping Autoencoder: Learning a Disentangled Structure-texture Representation of Chest Radiographs
Lei Zhou, Joseph Bae, Huidong Liu +5
Well-labeled datasets of chest radiographs (CXRs) are difficult to acquire due to the high cost of annotation. Thus, it is desirable to learn a robust and transferable representati…
Attention-based Multi-scale Gated Recurrent Encoder with Novel Correlation Loss for COVID-19 Progression Prediction
Aishik Konwer, Joseph Bae, Gagandeep Singh +5
COVID-19 image analysis has mostly focused on diagnostic tasks using single timepoint scans acquired upon disease presentation or admission. We present a deep learning-based approa…