390 citations · 472 across the 32 of their papers we have counts for
10 papers · 1 filter
GliomaINK: A Tumor and Anatomy Conditioned Flow Matching Framework for 3D MRI Sequence Synthesis in Glioma Patients
Moinak Bhattacharya, Annie Singh, Mohammad Chaudhry +2
Background: Incomplete multiparametric MR examinations limit glioma characterization and downstream AI tools. Generative models can synthesize missing MRI sequences, but anatomical…
Learning to Segment from Noisy Annotations: A Spatial Correction Approach
Jiachen Yao, Yikai Zhang, Songzhu Zheng +3
Noisy labels can significantly affect the performance of deep neural networks (DNNs). In medical image segmentation tasks, annotations are error-prone due to the high demand in ann…
SAM-Path: A Segment Anything Model for Semantic Segmentation in Digital Pathology
Jingwei Zhang, Ke Ma, Saarthak Kapse +4
Semantic segmentations of pathological entities have crucial clinical value in computational pathology workflows. Foundation models, such as the Segment Anything Model (SAM), have…
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…
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…
Attention based CNN-LSTM Network for Pulmonary Embolism Prediction on Chest Computed Tomography Pulmonary Angiograms
Sudhir Suman, Gagandeep Singh, Nicole Sakla +5
With more than 60,000 deaths annually in the United States, Pulmonary Embolism (PE) is among the most fatal cardiovascular diseases. It is caused by an artery blockage in the lung;…
Radiomic Deformation and Textural Heterogeneity (R-DepTH) Descriptor to characterize Tumor Field Effect: Application to Survival Prediction in Glioblastoma
Marwa Ismail, Prateek Prasanna, Kaustav Bera +10
The concept of tumor field effect implies that cancer is a systemic disease with its impact way beyond the visible tumor confines. For instance, in Glioblastoma (GBM), an aggressiv…