AI can help Pathologists quickly extract insights from Pathological imagery like Tissue Slides, leading to much faster and more accurate diagnosis. This can help them scale their workloads instead of spending significant time manually analyzing those imagesÂ
Understand the Use-case under 5 minutes
Video (2 minutes)
The evolution of Pathology from Microscope based workflows to Digital ones, and the role of AI in scaling slide analysis at light speed - especially with large amounts of unlabelled data
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Video (4 minutes)
Royal Philips and PathAI, a company that develops AI for pathology, are collaborating with the aim to develop solutions that improve the precision and accuracy of routine diagnosis of cancer and other diseases.
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Video (5 minutes)
Watch Liron Pantanowitz, MD, of the University of Pittsburgh Medical Center (UPMC), discuss the benefits of implementing AI in pathology and his experience from a validation study for analyzing prostate biopsies.
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Get to know more Business and Technical details about the use-case (15-30 minutes)
More detailed introduction covering business and technical aspects
Video (21 minutes)
How Pathologists can make use of AI to streamline their work, showing different examples like and ROI achieved from each
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Video (21 minutes)
Watch Prof. Nasir discusses the role of AI in Pathology and how pathology is going digital, and answers the question: what is the potential role of AI in pathology?
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Video (23 minutes)
A Brief Intro to Digital Pathology And Common AI Techniques Such As Classification, Search, And More. Watch starting 23:55 for the role of AI in Pathology
WatchCase studies, Organizational Aspects, Return on Investment examples
Article (4 minutes)
Summarizing the Benefits of leveraging AI in digital pathology with 5 case studies: Scaling Productivity, Increasing Diagnostic Accuracy, Reducing Costs, Enhancing Staff Satisfaction, & more
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Article (4 minutes)
Summarizing the role of AI in Digital Pathology, mentioning specific case studies and ROI achieved
ReadMore details on the technical aspects of the use-case
Video (12 minutes)
Explanation for some of the common AI techniques and algorithms for Digital Pathology: Classification, Segmentation, Content-Based Image Retrieval(Image Search), Medical Image Captioning, and Information Fusion
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Paper
This review describes clinical perspectives and discusses the statistical methods, clinical applications, potential obstacles, and future directions of AI in computational pathology
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Article (44 minutes)
Explaining the common Deep Learning methods in Pathology like UNet, GANs, and Unsupervised Learning. Expands on some applications of AI like Prostate Cancer, Metastasis Detection in Breast Cancer, & more
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Technical resources that will help you implement the use-case (notebooks, tutorials..)
Github Repo
A Deep-Learning-Based Pipeline For Data-Efficient And Weakly Supervised Whole-Slide-Level Analysis For Segmentation
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Fastpathology is An Open-Source Platform For Deep Learning-Based Research and Decision Support in Digital Pathology
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Paper
Introducing a scalable Crowdsourcing approach & dataset for Nucleus Classification, Localization and Segmentation in Breast Cancer, and And Explains the deep learning approach used
ReadGithub Repo
A software application built on top of openslide for viewing whole slide images (WSI) and performing pathological analysis. State of the art cancer AI pipeline to segment and display the cancerous tissue regions
VisitGithub Repo
A solution for machine learning for pathologists (easy semantic segmentation of WSI) This is a library of codes for iterative training of the DeepLab v2 semantic segmentation network for WSIs
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The aim of this project is to provide a tool for WSI processing in a reproducible environment to support clinical and scientific research. It can handle WSIs, detect the tissue, and retrieve informative tiles
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Data Sets you can use to build Demos, POCs, or test Algorithms
Breast Cancer Metastases In Lymph Nodes Provided By Grand Challenge For Detection And Classification Of Breast Cancer Metastases In Whole-Slide Images Of Histological Lymph Node Sections
25,000 Images Of 5 Classes Including Lung And Colon Cancer & Healthy Samples. There Are Five Classes In The Dataset, each with 5,000 Images
The Breast Cancer Histopathological Image Classification (Breakhis) Is Composed Of 9,109 Microscopic Images Of Breast Tumor Tissue Collected From 82 Patients Using Different Magnifying Factors
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