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Found 15 papers, 3 papers with code
Implicit Image-to-Image Schrodinger Bridge for CT Super-Resolution and Denoising
As a promising alternative, the Image-to-Image Schr\"odinger Bridge (I2SB) initializes the generative process from corrupted images and integrates training techniques from conditional diffusion models.
MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image Segmentation
The Segment Anything Model (SAM), a foundation model for general image segmentation, has demonstrated impressive zero-shot performance across numerous natural image segmentation tasks.
CohortGPT: An Enhanced GPT for Participant Recruitment in Clinical Study
Participant recruitment based on unstructured medical texts such as clinical notes and radiology reports has been a challenging yet important task for the cohort establishment in clinical research.
Trained Model in Supervised Deep Learning is a Conditional Risk Minimizer
We proved that a trained model in supervised deep learning minimizes the conditional risk for each input (Theorem 2. 1).
Development and Validation of a Deep Learning Model for Prediction of Severe Outcomes in Suspected COVID-19 Infection
Predictive risk scores for COVID-19 severe outcomes ("CO-RISK" score) were derived from model output and evaluated on the testing dataset, as well as compared to human performance.
Deep Metric Learning-based Image Retrieval System for Chest Radiograph and its Clinical Applications in COVID-19
These results demonstrate our deep metric learning based image retrieval model is highly efficient in the CXR retrieval, diagnosis and prognosis, and thus has great clinical value for the treatment and management of COVID-19 patients.
Deep Learning-based Four-region Lung Segmentation in Chest Radiography for COVID-19 Diagnosis
A segmentation model to separate left and right lung is firstly applied, and then a carina and left hilum detection network is used, which are the clinical landmarks to separate the upper and lower lungs.
Learning to Scan: A Deep Reinforcement Learning Approach for Personalized Scanning in CT Imaging
It is more desirable to design a personalized scanning strategy for each subject to obtain better reconstruction result.
Deep Learning Based Detection and Localization of Intracranial Aneurysms in Computed Tomography Angiography
Purpose: To develop CADIA, a supervised deep learning model based on a region proposal network coupled with a false-positive reduction module for the detection and localization of intracranial aneurysms (IA) from computed tomography angiography (CTA), and to assess our model's performance to a similar detection network.
Self-supervised Dynamic CT Perfusion Image Denoising with Deep Neural Networks
It is necessary to reduce the dose of CTP for routine applications due to the high radiation exposure from the repeated scans, where image denoising is necessary to achieve a reliable diagnosis.
Consensus Neural Network for Medical Imaging Denoising with Only Noisy Training Samples
In this paper we proposed a training method which learned denoising neural networks from noisy training samples only.
Computationally Efficient Deep Neural Network for Computed Tomography Image Reconstruction
The purpose of this work is to reduce the memory and time consumption of the training of the reconstruction networks for CT to make it practical for current hardware, while maintaining the quality of the reconstructed images.
End-to-End Abnormality Detection in Medical Imaging
To align the acquisition with the annotations made by radiologists in the image domain, a DNN was built as the unrolled version of iterative reconstruction algorithms to map the acquisitions to images, and followed by a 3D convolutional neural network (CNN) to detect the abnormality in the reconstructed images.
End-to-end Lung Nodule Detection in Computed Tomography
With 144 multi-slice fanbeam pro-jections, the proposed end-to-end detector could achieve comparable sensitivity with the reference detector, which was trained and applied on the fully-sampled image data.
A Cascaded Convolutional Neural Network for X-ray Low-dose CT Image Denoising
Image denoising techniques are essential to reducing noise levels and enhancing diagnosis reliability in low-dose computed tomography (CT).
