5/3/2023 0 Comments Deepfocus ioJust let me know below if you want to be one of those beta testers, I'll get back to you via PN. We are happy to welcome some early beta testers. If you want your software strategy and concept (or parts of it) to be generated with a few clicks. If you want to get your marketing strategy and content generated with a few clicks. This is a 100x boost in productivity, unbelievable. We even created a video with AI showing a talking head of an artifically generated person and this ad already won us clients through our funnel. In only 20 days we created an AI-based software that uses your idea for a digital product (App etc) to generate a software concept, compares tech stacks for you, creates implementation concept AND even generates working and well written software code as output.Īdditionally we've created an AI-based marketing strategy generator that generates the marketing strategy and the content for an entire funnel to sell your services or products automatically online. 2011 6(1):S16.I'm TOTALLY flashed, you won't believe it (I even don't). Distributed computing in image analysis using open source frameworks and application to image sharpness assessment of histological whole slide images. The source code and network models were implemented with TensorFlow with 32-bit precision. Referenceless image quality evaluation for whole slide imaging. DeepFocus This repository provides source code, network models, and datasets (17GB) for the DeepFocus project from Facebook Reality Labs. Hashimoto N, Bautista PA, Yamaguchi M, Ohyama N, Yagi Y. Quality evaluation of virtual slides using methods based on comparing common image areas. Image quality metrics applied to digital pathology SPIE Photonics Europe 2016: International Society for Optics and Photonics. Jiménez A, Bueno G, Cristóbal G, Déniz O, Toomey D, Conway C, editors. An automated blur detection method for histological whole slide imaging. Moles Lopez X, D'Andrea E, Barbot P, Bridoux AS, Rorive S, Salmon I, et al. DeepFocus has the potential to be integrated with whole slide scanners to automatically re-scan problematic areas, hence improving the overall image quality for pathologists and image analysis algorithms. When trained and tested on two independent datasets, DeepFocus resulted in an average accuracy of 93.2% (± 9.6%), which is a 23.8% improvement over an existing method. DeepFocus was trained by using 16 different H&E and IHC-stained slides that were systematically scanned on nine different focal planes, generating 216,000 samples with varying amounts of blurriness. DeepFocus is built on TensorFlow, an open source library that exploits data flow graphs for efficient numerical computation. The aim of this study is to develop a deep learning based software called, DeepFocus, which can automatically detect and segment blurry areas in digital whole slide images to address these problems. Moreover, this process is both tedious, and time-consuming. These areas are typically identified by visual inspection, which leads to a subjective evaluation causing high intra- and inter-observer variability. DeepFocus This repository provides source code, network models, and datasets (17GB) for the DeepFocus project from Facebook Reality Labs. Moreover, these artifacts hamper the performance of computerized image analysis systems. Unfortunately, whole slide scanners often produce images with out-of-focus/blurry areas that limit the amount of tissue available for a pathologist to make accurate diagnosis/prognosis. The development of whole slide scanners has revolutionized the field of digital pathology.
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