Pulmonary Embolism Detection

3 papers with code • 1 benchmarks • 0 datasets

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PECon: Contrastive Pretraining to Enhance Feature Alignment between CT and EHR Data for Improved Pulmonary Embolism Diagnosis

biomedia-mbzuai/pecon 27 Aug 2023

Previous deep learning efforts have focused on improving the performance of Pulmonary Embolism(PE) diagnosis from Computed Tomography (CT) scans using Convolutional Neural Networks (CNN).

4
27 Aug 2023

Seeking an Optimal Approach for Computer-Aided Pulmonary Embolism Detection

Nahid1992/CAD_PE 15 Sep 2021

At the image level, we compare convolutional neural networks (CNNs) with vision transformers, and contrast self-supervised learning (SSL) with supervised learning, followed by an evaluation of transfer learning compared with training from scratch.

5
15 Sep 2021

Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis

MrGiovanni/ModelsGenesis 19 Aug 2019

More importantly, learning a model from scratch simply in 3D may not necessarily yield performance better than transfer learning from ImageNet in 2D, but our Models Genesis consistently top any 2D approaches including fine-tuning the models pre-trained from ImageNet as well as fine-tuning the 2D versions of our Models Genesis, confirming the importance of 3D anatomical information and significance of our Models Genesis for 3D medical imaging.

718
19 Aug 2019