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Dataset results
8 results for “Whole Slide Image Analysis”
Complete results of Procedure section in manuscript "PathML: A unified framework for whole-slide image analysis with deep learning"
<p>The results from a complete run of the Procedure section of the paper "PathML: A unified framework for whole-slide image analysis with deep learning".</p>
High resolution images for 'Identification of "BRAF-positive" cases based on whole-slide image analysis'
<p>This archive contains high resolution images to accompany the article 'Identification of “BRAF-positive” cases based on whole-slide image analysis' by V. Popovici, A. Krenek and E. Budinska.</p>
Analysis of multiplexed whole slide images with QuPath and Cytomap
<p>This image was acquired during MIFOBIO 2023 and was used for workshop entitled "Analysis of multiplexed whole slide images with QuPath and Cytomap".</p>
Orbit Image Analysis: An open-source whole slide image analysis tool
<p>This is a whole slide image (WSI) dataset for glomeruli segmentation on kidney tissue, in total 88 images.</p> <p>The train-set (58 images) and test-set (32 images) has been used in the publication "Orbit Image Analysis: An open-source whole slide image analysis tool" to train and test the<br> glomeruli segmentation model.</p>
Trained CNN for analysis of melanoma whole slide images to automatically assess the infiltration of TILs
<p>The file is the trained convolutional neural network (CNN) developed in "Ugolini F et al.Tumor infiltrating lymphocytes recognition in primary melanoma by deep learning convolutional neural network" submitted to American Journal of Pathology (2023). The CNN is based on a a pre-trained Inception-ResNet-v2 to automatically to recognize areas in the tumor region containing TILs and area without TILs in histopathological digitalized slides of primary melanoma. The file is in a Matlab format (.mat).</p>
Orbit Image Analysis: an open-source whole slide image analysis tool
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Data from: High-throughput adaptive sampling for whole-slide histopathology image analysis (HASHI) via convolutional neural networks: application to invasive breast cancer detection
Precise detection of invasive cancer on whole-slide images (WSI) is a critical first step in digital pathology tasks of diagnosis and grading. Convolutional neural network (CNN) is the most popular representation learning method for computer vision tasks, which have been successfully applied in digital pathology, including tumor and mitosis detection. However, CNNs are typically only tenable with relatively small image sizes (200x200 pixels). Only recently, Fully convolutional networks (FCN) are able to deal with larger image sizes (500x500 pixels) for semantic segmentation. Hence, the direct application of CNNs to WSI is not computationally feasible because for a WSI, a CNN would require billions or trillions of parameters. To alleviate this issue, this paper presents a novel method, High-throughput Adaptive Sampling for whole-slide Histopathology Image analysis (HASHI), which involves: i) a new efficient adaptive sampling method based on probability gradient and quasi-Monte Carlo sampling, and, ii) a powerful representation learning classifier based on CNNs. We applied HASHI to automated detection of invasive breast cancer on WSI. HASHI was trained and validated using three different data cohorts involving near 500 cases and then independently tested on 195 studies from The Cancer Genome Atlas. The results show that (1) the adaptive sampling method is an effective strategy to deal with WSI without compromising prediction accuracy by obtaining comparative results of a dense sampling (~6 million of samples in 24 hours) with far fewer samples (~2,000 samples in 1 minute), and (2) on an independent test dataset, HASHI is effective and robust to data from multiple sites, scanners, and platforms, achieving an average Dice coefficient of 76%.
Data from: High-throughput adaptive sampling for whole-slide histopathology image analysis (HASHI) via convolutional neural networks: application to invasive breast cancer detection
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