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Found 24 papers, 11 papers with code
The Liver Tumor Segmentation Benchmark (LiTS)
In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018.
Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks
In the first step, we train a FCN to segment the liver as ROI input for a second FCN.
Automatic Liver And Tumor Segmentation
Lesion Segmentation
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A Computed Tomography Vertebral Segmentation Dataset with Anatomical Variations and Multi-Vendor Scanner Data
With the advent of deep learning algorithms, fully automated radiological image analysis is within reach.
SurvivalNet: Predicting patient survival from diffusion weighted magnetic resonance images using cascaded fully convolutional and 3D convolutional neural networks
A 3D neural network (SurvivalNet) then predicts the HCC lesions' malignancy from the HCC tumor segmentation.
Xplainer: From X-Ray Observations to Explainable Zero-Shot Diagnosis
Automated diagnosis prediction from medical images is a valuable resource to support clinical decision-making.
Differentially private federated deep learning for multi-site medical image segmentation
The application of PTs to FL in medical imaging and the trade-offs between privacy guarantees and model utility, the ramifications on training performance and the susceptibility of the final models to attacks have not yet been conclusively investigated.
3D Arterial Segmentation via Single 2D Projections and Depth Supervision in Contrast-Enhanced CT Images
In this work, we propose a novel method to segment the 3D peripancreatic arteries solely from one annotated 2D projection per training image with depth supervision.
Longitudinal Quantitative Assessment of COVID-19 Infection Progression from Chest CTs
Chest computed tomography (CT) has played an essential diagnostic role in assessing patients with COVID-19 by showing disease-specific image features such as ground-glass opacity and consolidation.
Private, fair and accurate: Training large-scale, privacy-preserving AI models in medical imaging
In this work, we evaluated the effect of privacy-preserving training of AI models regarding accuracy and fairness compared to non-private training.
Interpretable 2D Vision Models for 3D Medical Images
Training Artificial Intelligence (AI) models on 3D images presents unique challenges compared to the 2D case: Firstly, the demand for computational resources is significantly higher, and secondly, the availability of large datasets for pre-training is often limited, impeding training success.
Efficient, high-performance pancreatic segmentation using multi-scale feature extraction
For artificial intelligence-based image analysis methods to reach clinical applicability, the development of high-performance algorithms is crucial.
Differential Diagnosis for Pancreatic Cysts in CT Scans Using Densely-Connected Convolutional Networks
The lethal nature of pancreatic ductal adenocarcinoma (PDAC) calls for early differential diagnosis of pancreatic cysts, which are identified in up to 16% of normal subjects, and some of which may develop into PDAC.
Privacy-preserving medical image analysis
The utilisation of artificial intelligence in medicine and healthcare has led to successful clinical applications in several domains.
Differentially private training of neural networks with Langevin dynamics for calibrated predictive uncertainty
We show that differentially private stochastic gradient descent (DP-SGD) can yield poorly calibrated, overconfident deep learning models.
Sensitivity analysis in differentially private machine learning using hybrid automatic differentiation
Reconciling large-scale ML with the closed-form reasoning required for the principled analysis of individual privacy loss requires the introduction of new tools for automatic sensitivity analysis and for tracking an individual's data and their features through the flow of computation.
U-GAT: Multimodal Graph Attention Network for COVID-19 Outcome Prediction
Specifically, we introduce a multimodal similarity metric to build a population graph for clustering patients and an image-based end-to-end Graph Attention Network to process this graph and predict the COVID-19 patient outcomes: admission to ICU, need for ventilation and mortality.
Interactive Segmentation for COVID-19 Infection Quantification on Longitudinal CT scans
Existing automatic and interactive segmentation models for medical images only use data from a single time point (static).
Longitudinal Self-Supervision for COVID-19 Pathology Quantification
For this purpose, longitudinal self-supervision schemes are explored on clinical longitudinal COVID-19 CT scans.
Exploiting segmentation labels and representation learning to forecast therapy response of PDAC patients
The prediction of pancreatic ductal adenocarcinoma therapy response is a clinically challenging and important task in this high-mortality tumour entity.
Current State of Community-Driven Radiological AI Deployment in Medical Imaging
To address the barrier to clinical deployment, we have formed MONAI Consortium, an open-source community which is building standards for AI deployment in healthcare institutions, and developing tools and infrastructure to facilitate their implementation.
Atlas-Based Interpretable Age Prediction In Whole-Body MR Images
We utilise the Grad-CAM interpretability method to determine the body areas most predictive of a person's age.
Body Fat Estimation from Surface Meshes using Graph Neural Networks
Body fat volume and distribution can be a strong indication for a person's overall health and the risk for developing diseases like type 2 diabetes and cardiovascular diseases.
Constructing Population-Specific Atlases from Whole Body MRI: Application to the UKBB
We demonstrate different applications of these atlases, using the differences between subjects with medical conditions such as diabetes and cardiovascular diseases and healthy subjects from the atlas space.
Reconciling AI Performance and Data Reconstruction Resilience for Medical Imaging
Although a lower budget decreases the risk of information leakage, it typically also reduces the performance of such models.
