Features: 10 OFF for new users limited offer. We observed a relevant relation between computational learning and experts' insights, demonstrating how human knowledge may influence the correct computational learning.Īdenocarcinoma Barrett's esophagus Computer-aided diagnosis Explainable artificial intelligence Machine learning.Ĭopyright © 2021 The Author(s). With the LightInTheBox App’s intuitive interface, you can tap into our amazing line of products to purchase items on the go. The results also lightened that the higher the model's sensitivity, the stronger the correlation of human and computational segmentation agreement. Moreover, there is moderate to high correlation between the sensitivity of a model and the human-and-computer agreement.
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We could show that saliency attributes match best with the manual experts' delineations. A total of 37 of reviewers gave a Poor or Bad rating with 34 of them giving just 1 star. Just 50 of those who wrote reviews for Light In The Box Limited gave the company an Excellent rating. Keep in mind, Light In The Box has an F rating of 1.41 out of 5 from the BBB.org website, because of complaints from customer service, sizes on some clothes seem to run smaller, and some. Four Convolutional Neural Network models (AlexNet, SqueezeNet, ResNet50, and VGG16) were analyzed using five different interpretation techniques (saliency, guided backpropagation, integrated gradients, input × gradients, and DeepLIFT) to compare their agreement with experts' previous annotations of cancerous tissue. Out of 24,506 reviews on Trustpilot, its LightInTheBox review gave an overall score of 2.5 out of 5. is a legit and safe site, which was founded in 2007 out of China, and has a current Light In The Box review rating of 6.6 out of 10 from us. Hence, we aim to investigate the use of explainable artificial intelligence techniques to quantitatively highlight discriminative regions during the classification of early-cancerous tissues in Barrett's esophagus-diagnosed patients. For this task, the black-box nature of deep learning techniques must be lightened up to transfer its promising results into clinical practice.
The reliability related to machine learning predictions must be explained and interpreted, especially if diagnosis support is addressed. Even though artificial intelligence and machine learning have demonstrated remarkable performances in medical image computing, their level of accountability and transparency must be provided in such evaluations.
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