Integrating the two platforms provides pathologists instant access to quantitative image analysis right from Corista’s digital pathology workflow suite Concord, MA (February 11, 2020) –
4 March 2020 | In this webinar, researchers and pathologists from the University of New Mexico will discuss their recently published study using the HALO AI deep learning convolutional neural network to detect and classify different types of gastric injury.
Using Deep Learning to Recognize Common Patterns of Injury in Gastric Pathology Read More »
Hoa Hoang Ngoc Pham, et al, The American Journal of Pathology, 2020
Pham and colleagues set out to address high false positivity of lymph nodes metastasis analysis using deep learning. As characterizing lymph node metastases in breast and lung cancer is of great clinical importance for treatment selection and prognosis, finding a method with high sensitivity and specificity would represent a major advance. Here, the researchers demonstrate a two-step approach with HALO AI where the first deep learning algorithm excludes the lymph germinal centers that are the source of false positivity and the second algorithm detects tumor cells. The researchers demonstrate this method on lung cancer lymph tissue and find a sensitivity ~78% and specificity ~97% and conclude that a two-step approach can successfully be used to detect lung cancer metastases to the lymph nodes with high specificity. Future research may target development of an algorithm or algorithms with increased sensitivity that maintain high specificity.
David R. Martin, et al, Archives of Pathology & Laboratory Medicine, 2019
Researchers from the University of New Mexico set out to investigate tissue classification using deep learning to evaluate nonneoplastic gastric biopsies. Ground truth diagnosis was established by gastrointestinal pathologists. HALO AI was trained to recognize Helicobacter pylori (H pylori) mediated gastritis, chemical gastropathy, normal mucosa, smooth muscle, and glass. The HALO AI classifier showed high sensitivity and specificity for control biopsies and gastropathy cases and represents the first deep learning driven evaluation of inflammatory gastrointestinal pathology published. The sensitivity and specificity was as follows: normal tissue (73.7% and 79.6%), H pylori (95.7% and 100%), and reactive gastropathy (100% and 62.5%). Martin and colleagues conclude that a convolutional neural network such as HALO AI can function as a screening aid for H pylori gastritis.
11 December 2019 | Introducing HALO AP™️ – next generation digital pathology software for the modern Anatomic Pathology lab.
Indica Labs is proud to launch HALO AP, a state-of-the-art anatomic pathology software designed to support a variety of clinical workflows. HALO AP builds upon tried-and-true HALO analytics, artificial intelligence, and unmatched usability, scalability and performance.
Introducing HALO AP – Workflow Solutions for Anatomic Pathology Read More »
Indica Labs invites you to attend our London Quantitative Pathology and HALO User Group Meeting Wednesday, 4 December 2019 London Heathrow Marriott Hotel London, UK
Indica Labs’ London Quantitative Pathology and HALO User Group Meeting Read More »
20 November 2019 | Core laboratories provide a vital service to researchers by offering expert advice on advanced technologies, shared access to specialized instrumentation and software, as well support for specific types of assays. Increasingly, digital slide scanners and quantitative analysis platforms are being centralized into these core labs to give users access to advanced technologies for both fluorescent and brightfield digital pathology.
Spatially resolved immunophenotyping using artificial intelligence and the HALO image analysis platform In this one-hour pre-conference workshop, Indica Labs’ is pleased to have Vaishali Shinde,
Indica Labs Preconference Workshop – Path Visions, Orlando, FL USA – October 6, 2019 Read More »
17 July 2019 | In this one-hour webinar, we will demonstrate some of the possibilities of the HALO® GraphQL API. The database powering a HALO® or HALO® LinkTM installation stores a wealth of information about image metadata, analysis results, annotations, and more.
Data Exploration and Third-Party Integrations with the HALO® GraphQL API Read More »
11 June 2019 | In this webinar, Dr Anna Martirosyan and Dr. Emmanuel Prestat will introduce the Brightplex platform, including a description of the MDSC Panel, the workflow and techniques employed. A discussion on how the HALO(R) image analysis platform is integrated as an image processing component of the workflow, and how the technology can be applied to investigate the immune contexture of tumors will follow.
