The Non-Small Cell Lung Cancer (NSCLC) H&E Tumor Tissue Detection App is a pre-trained HALO AI masking classifier designed to segment tumor, stroma, necrosis/other, and glass across H&E-stained whole slide images. Outputs include the area of tissue falling into each of the four classes. The App can classify tissues within regions of interest, fields of view, or entire images and can be combined with HALO® Analysis Modules and other HALO AI Apps to derive further quantitative outputs, as demonstrated in the application shown below. As an RUO product, NSCLC H&E Tumor Tissue Detection App can be further trained, tuned, and improved by end users using their own images and data.
Intended Use
For Research Use Only and not intended for clinical diagnostic use.
What's Included?
- NSCLC H&E Tumor Tissue Detection App
Training Information
- The NSCLC H&E Tumor Tissue Detection App training was performed using 6,200+ annotations from hundreds of hematoxylin- and eosin-stained whole slide images of NSCLC cancer.
- Tissues were obtained from multiple institutes using different scanner types to improve overall generalizability of cancer cell detection. Note, not all of the file formats shown under ‘File Format Compatibility’ tab were included in the training set.
Prerequisites
All pre-trained HALO AI Apps require an existing license of HALO and HALO AI upgraded to version 4.0.5.
File formats supported by the HALO image analysis platform:
- Non-proprietary (JPG, TIF, OME.TIFF)
- Nikon (ND2)
- 3D Histech (MRXS)
- Akoya (QPTIFF, component TIFF)
- Olympus / Evident (VSI)
- Hamamatsu (NDPI, NDPIS)
- Aperio (SVS, AFI)
- Zeiss (CZI)
- Leica (SCN, LIF)
- Ventana (BIF)
- Philips (iSyntax, i2Syntax)
- KFBIO (KFB, KFBF)
- DICOM (DCM*)
*whole-slide images
Accelerate your AI Development
AI is a powerful tool in your image analysis toolbox, but AI development is time consuming and data intensive. Pre-trained using hundreds of images and training annotations, HALO AI Apps give users a jumpstart to accelerate AI development.
Refine with your Training Data
For most applications, HALO AI Apps will work right out-of-the-box, but we realize that it is impossible to test every application. Importantly, HALO AI Apps are ‘open’ and can be further trained by users to optimize and refine performance for specific applications and stains.
Complement your Expertise
HALO AI Apps are designed to handle time-consuming and tedious tasks and providing consistent, standardized measurements. You are free to apply your scientific expertise where it’s needed most – in the interpretation of data to make informed decisions.
Optimized for Generalizability and Flexibility
Having been trained and tested on tissues with variable staining from different scanners and with multi-institutional data, our HALO AI Apps are designed to achieve the highest level of generalizability right out-of-the-box, but with the flexibility to be trained further with your own data. Shown here, the NSCLC H&E Tumor Tissue Detection App used out-of-the-box to classify tumor in tissues with different morphologies and stain intensities.
Seamlessly Integrate with Other HALO AI Apps and HALO Image Analysis Modules
The NSCLC H&E Tumor Tissue Detection App can be easily added to HALO analysis modules or chained to our Cell Phenotyper Apps to automate the selection of specific tissue class(es) for analysis. In the examples shown here, the App is used along with the Pan-Cancer H&E Lymphocyte Cell Detection App to quantify tumor infiltrating lymphocytes (TILs). Although not shown here, the HALO Spatial Analysis Module can be used to quantify lymphocytes at user defined distances along the tumor margin if these measurements are desired.
Please note: HALO AI Apps and associated applications are intended for research use only. Please visit our Clinical Products page to discover clinical AI products deployable through our HALO AP® platform.
Free Proof-of-Concept Analysis
See HALO AI Apps in action on up to three of your images with a free proof-of-concept analysis.
Related HALO AI Apps
The NSCLC IHC Tumor Detection App is a pre-trained HALO AI classifier designed to detect, segment, and quantify tumor area and non-tumor area across hematoxylin and DAB-stained whole-slide digital images of NSCLC.
Learn More
The NSCLC IHC Cancer Cell Phenotyper App is a pre-trained HALO AI object phenotyper designed to detect, segment, and quantify non-cancer cells, IHC-positive cancer cells and IHC-negative cancer cells across hematoxylin and DAB-stained whole-slide digital images of NSCLC.
Learn More
The NSCLC H&E Cancer Cell Phenotyper App is a pre-trained object phenotyper designed to detect, quantify, and segment cancer cells from non-cancer cells across H&E-stained whole-slide digital images of NSCLC.
Learn MoreUse the arrows above to view additional related AI apps
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