757 research outputs found
MiniSeg: An Extremely Minimum Network for Efficient COVID-19 Segmentation
The rapid spread of the new pandemic, i.e., COVID-19, has severely threatened
global health. Deep-learning-based computer-aided screening, e.g., COVID-19
infected CT area segmentation, has attracted much attention. However, the
publicly available COVID-19 training data are limited, easily causing
overfitting for traditional deep learning methods that are usually data-hungry
with millions of parameters. On the other hand, fast training/testing and low
computational cost are also necessary for quick deployment and development of
COVID-19 screening systems, but traditional deep learning methods are usually
computationally intensive. To address the above problems, we propose MiniSeg, a
lightweight deep learning model for efficient COVID-19 segmentation. Compared
with traditional segmentation methods, MiniSeg has several significant
strengths: i) it only has 83K parameters and is thus not easy to overfit; ii)
it has high computational efficiency and is thus convenient for practical
deployment; iii) it can be fast retrained by other users using their private
COVID-19 data for further improving performance. In addition, we build a
comprehensive COVID-19 segmentation benchmark for comparing MiniSeg to
traditional methods
Deep learning for image-based liver analysis — A comprehensive review focusing on malignant lesions
Deep learning-based methods, in particular, convolutional neural networks and fully convolutional networks are now widely used in the medical image analysis domain. The scope of this review focuses on the analysis using deep learning of focal liver lesions, with a special interest in hepatocellular carcinoma and metastatic cancer; and structures like the parenchyma or the vascular system. Here, we address several neural network architectures used for analyzing the anatomical structures and lesions in the liver from various imaging modalities such as computed tomography, magnetic resonance imaging and ultrasound. Image analysis tasks like segmentation, object detection and classification for the liver, liver vessels and liver lesions are discussed. Based on the qualitative search, 91 papers were filtered out for the survey, including journal publications and conference proceedings. The papers reviewed in this work are grouped into eight categories based on the methodologies used. By comparing the evaluation metrics, hybrid models performed better for both the liver and the lesion segmentation tasks, ensemble classifiers performed better for the vessel segmentation tasks and combined approach performed better for both the lesion classification and detection tasks. The performance was measured based on the Dice score for the segmentation, and accuracy for the classification and detection tasks, which are the most commonly used metrics.publishedVersio
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