Search Results for author:
Found 17 papers, 5 papers with code
Surgical Phase Recognition in Laparoscopic Cholecystectomy
Automatic recognition of surgical phases in surgical videos is a fundamental task in surgical workflow analysis.
Federated Learning Enables Big Data for Rare Cancer Boundary Detection
Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data.
CD-Net: Histopathology Representation Learning using Pyramidal Context-Detail Network
To jointly leverage complementary information from multiple resolutions, we present a novel transformer based Pyramidal Context-Detail Network (CD-Net).
Self Pre-training with Masked Autoencoders for Medical Image Analysis
To validate the MAE self pre-training, we consider three diverse medical image tasks including chest X-ray disease classification, CT abdomen multi-organ segmentation and MRI brain tumor segmentation.
Temporal Context Matters: Enhancing Single Image Prediction with Disease Progression Representations
In our method, a self-attention based Temporal Convolutional Network (TCN) is used to learn a representation that is most reflective of the disease trajectory.
RadioTransformer: A Cascaded Global-Focal Transformer for Visual Attention-guided Disease Classification
However, deep learning frameworks, even those that rely on attention mechanisms, do not leverage this rich domain information.
Brain Cancer Survival Prediction on Treatment-na ive MRI using Deep Anchor Attention Learning with Vision Transformer
Image-based brain cancer prediction models, based on radiomics, quantify the radiologic phenotype from magnetic resonance imaging (MRI).
Lung Swapping Autoencoder: Learning a Disentangled Structure-texture Representation of Chest Radiographs
Well-labeled datasets of chest radiographs (CXRs) are difficult to acquire due to the high cost of annotation.
Attention-based Multi-scale Gated Recurrent Encoder with Novel Correlation Loss for COVID-19 Progression Prediction
This vector is used as an input to a decoder module to predict patch severity grades at a future timepoint.
Attention based CNN-LSTM Network for Pulmonary Embolism Prediction on Chest Computed Tomography Pulmonary Angiograms
In this study we propose a two-stage attention-based CNN-LSTM network for predicting PE, its associated type (chronic, acute) and corresponding location (leftsided, rightsided or central) on computed tomography (CT) examinations.
TopoTxR: A Topological Biomarker for Predicting Treatment Response in Breast Cancer
Characterization of breast parenchyma on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a challenging task owing to the complexity of underlying tissue structures.
Radiomic Deformation and Textural Heterogeneity (R-DepTH) Descriptor to characterize Tumor Field Effect: Application to Survival Prediction in Glioblastoma
Our work is based on the rationale that highly aggressive tumors tend to grow uncontrollably, leading to pronounced biomechanical tissue deformations in the normal parenchyma, which when combined with local morphological differences in the tumor confines on MRI scans, will comprehensively capture tumor field effect.
EventScore: An Automated Real-time Early Warning Score for Clinical Events
We also show that discretization improves model performance by comparing our model to a baseline logistic regression model.
Predicting Clinical Outcomes in COVID-19 using Radiomics and Deep Learning on Chest Radiographs: A Multi-Institutional Study
Radiomic and DL classification models had mAUCs of 0. 78+/-0. 02 and 0. 81+/-0. 04, compared with expert scores mAUCs of 0. 75+/-0. 02 and 0. 79+/-0. 05 for mechanical ventilation requirement and mortality prediction, respectively.
Spatial-And-Context aware (SpACe) "virtual biopsy" radiogenomic maps to target tumor mutational status on structural MRI
We evaluate the efficacy of SpACe maps on MRI scans with co-localized ground truth obtained from corresponding biopsy, to predict the mutation status of 2 driver genes in Glioblastoma: (1) EGFR (n=91), and (2) MGMT (n=81).
Can tumor location on pre-treatment MRI predict likelihood of pseudo-progression versus tumor recurrence in Glioblastoma? A feasibility study
In this study, 74 pre-treatment Glioblastoma MRI scans with PsP (33) and tumor recurrence (41) were analyzed.
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
