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Found 5 papers, 1 papers with code
Lung-Originated Tumor Segmentation from Computed Tomography Scan (LOTUS) Benchmark
Lung cancer is one of the deadliest cancers, and in part its effective diagnosis and treatment depend on the accurate delineation of the tumor.
Improving Reproducibility and Performance of Radiomics in Low Dose CT using Cycle GANs
The trained GANs were applied to three scenarios: 1) improving radiomics reproducibility in simulated low dose CT images and 2) same-day repeat low dose CTs (RIDER dataset) and 3) improving radiomics performance in survival prediction.
Generative Models Improve Radiomics Performance in Different Tasks and Different Datasets: An Experimental Study
Generative models can improve the performance of low dose CT-based radiomics in different tasks.
Generative Models Improve Radiomics Reproducibility in Low Dose CTs: A Simulation Study
Moreover, images with different noise levels can be denoised to improve the reproducibility using these models without re-training, as long as the noise intensity is equal or lower than that in high-noise CTs.
Lung Cancer Diagnosis Using Deep Attention Based on Multiple Instance Learning and Radiomics
In this article, we treat lung cancer diagnosis as a multiple instance learning (MIL) problem in order to better reflect the diagnosis process in the clinical setting and for the higher interpretability of the output.
