9 citations · 15 across the 7 of their papers we have counts for
7 papers
Token Sparsification for Faster Medical Image Segmentation
Lei Zhou, Huidong Liu, Joseph Bae +3
Can we use sparse tokens for dense prediction, e.g., segmentation? Although token sparsification has been applied to Vision Transformers (ViT) to accelerate classification, it is s…
Enhancing Modality-Agnostic Representations via Meta-Learning for Brain Tumor Segmentation
Aishik Konwer, Xiaoling Hu, Joseph Bae +3
In medical vision, different imaging modalities provide complementary information. However, in practice, not all modalities may be available during inference or even training. Prev…
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…
Self Pre-training with Masked Autoencoders for Medical Image Classification and Segmentation
Lei Zhou, Huidong Liu, Joseph Bae +3
Masked Autoencoder (MAE) has recently been shown to be effective in pre-training Vision Transformers (ViT) for natural image analysis. By reconstructing full images from partially…
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…
Predicting Clinical Outcomes in COVID-19 using Radiomics and Deep Learning on Chest Radiographs: A Multi-Institutional Study
Joseph Bae, Saarthak Kapse, Gagandeep Singh +10
We predict mechanical ventilation requirement and mortality using computational modeling of chest radiographs (CXRs) for coronavirus disease 2019 (COVID-19) patients. This two-cent…