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Found 12 papers, 1 papers with code
Using YOLO v7 to Detect Kidney in Magnetic Resonance Imaging
The best primary model was then used to identify tumors in the remaining 861 patients and bounding box coordinates were generated on their scans using the model.
Automatic segmentation of clear cell renal cell tumors, kidney, and cysts in patients with von Hippel-Lindau syndrome using U-net architecture on magnetic resonance images
Our 2D U-Net achieved an average ccRCC lesion detection Area under the curve (AUC) of 0. 88 and DSC scores of 0. 78, 0. 40, and 0. 46 for segmentation of the kidney, cysts, and tumors, respectively.
Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation
Federated learning (FL) is a distributed machine learning technique that enables collaborative model training while avoiding explicit data sharing.
Improving Pneumonia Localization via Cross-Attention on Medical Images and Reports
Both the classification and localization are trained in conjunction and once trained, the model can be utilized for both the localization and characterization of pneumonia using only the input image.
Auto-FedAvg: Learnable Federated Averaging for Multi-Institutional Medical Image Segmentation
In this work, we design a new data-driven approach, namely Auto-FedAvg, where aggregation weights are dynamically adjusted, depending on data distributions across data silos and the current training progress of the models.
Information Bottleneck Attribution for Visual Explanations of Diagnosis and Prognosis
We provide an innovative visual explanation algorithm for general purpose and as an example application, we demonstrate its effectiveness for quantifying lesions in the lungs caused by the Covid-19 with high accuracy and robustness without using dense segmentation labels.
Federated Semi-Supervised Learning for COVID Region Segmentation in Chest CT using Multi-National Data from China, Italy, Japan
To facilitate CT analysis, recent efforts have focused on computer-aided characterization and diagnosis, which has shown promising results.
Weakly supervised one-stage vision and language disease detection using large scale pneumonia and pneumothorax studies
The architectural modifications address three obstacles -- implementing a supervised vision and language detection method in a weakly supervised fashion, incorporating clinical referring expression natural language information, and generating high fidelity detections with map probabilities.
DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation
We propose and evaluate several variations of deep ConvNets in the context of hierarchical, coarse-to-fine classification on image patches and regions, i. e. superpixels.
A Bottom-up Approach for Pancreas Segmentation using Cascaded Superpixels and (Deep) Image Patch Labeling
We present a bottom-up approach for pancreas segmentation in abdominal CT scans that is based on a hierarchy of information propagation by classifying image patches at different resolutions; and cascading superpixels.
A Bottom-Up Approach for Automatic Pancreas Segmentation in Abdominal CT Scans
Organ segmentation is a prerequisite for a computer-aided diagnosis (CAD) system to detect pathologies and perform quantitative analysis.
