Lung Adeno/Squam v1 Model Card

This model card describes the model associated with the manuscript "Uncertainty-informed deep learning models enable high-confidence predictions for digital histopathology", by Dolezal et al, available here.

Model Details

Uses

Examples

For direct use, the model can be loaded using Tensorflow/Keras:

import tensorflow as tf
model = tf.keras.models.load_model('/path/')

or loaded with Slideflow version 1.1+ with the following syntax:

import slideflow as sf
model = sf.model.load('/path/')

The stain normalizer can be loaded and fit using Slideflow:

normalizer = sf.util.get_model_normalizer('/path/')

The stain normalizer has a native Tensorflow transform and can be directly applied to a tf.data.Dataset:

# Map the stain normalizer transformation
# to a tf.data.Dataset
dataset = dataset.map(normalizer.tf_to_tf)

Alternatively, the model can be used to generate predictions for whole-slide images processed through Slideflow in an end-to-end Project. To use the model to generate predictions on data processed with Slideflow, simply pass the model to the Project.predict() function:

import slideflow
P = sf.Project('/path/to/slideflow/project')
P.predict('/model/path')

Direct Use

This model is intended for research purposes only. Possible research areas and tasks include

Excluded uses are described below.

Misuse and Out-of-Scope Use

This model should not be used in a clinical setting to generate predictions that will be used to inform patients, physicians, or any other health care members directly involved in their health care outside the context of an approved research protocol. Using the model in a clinical setting outside the context of an approved research protocol is a misuse of this model. This includes, but is not limited to:

Limitations

The model has not been validated to discriminate lung adenocarcinoma vs. squamous cell carcinoma in contexts where other tumor types are possible (such as lung small cell carcinoma, neuroendocrine tumors, metastatic deposits, etc.)

Bias

This model was trained on The Cancer Genome Atlas (TCGA), which contains patient data from communities and cultures which may not reflect the general population. This datasets is comprised of images from multiple institutions, which may introduce a potential source of bias from site-specific batch effects (Howard, 2021). The model was validated on data from the Clinical Proteomics Tumor Analysis Consortium (CPTAC) and an institutional dataset from Mayo Clinic, the latter of which consists primarily of data from patients of white and western cultures.

Training

Training Data The following dataset was used to train the model:

This model was trained on the full dataset, with a total of 941 slides.

Training Procedure Each whole-slide image was sectioned into smaller images in a grid-wise fashion in order to extract tiles from whole-slide images at 302 x 302 μm. Image tiles were extracted at the nearest downsample layer, and resized to 299 x 299 px using Libvips. During training,

During inference,

Tile-level and slide-level uncertainty thresholds are calculated and applied as discussed in the Paper. For this model, θ_tile=0.0228 and θ_slide=0.0139.

Evaluation Results

External evaluation results in the CPTAC and Mayo Clinic dataset are presented in the Paper and shown here:

Figure 4

Citation

@ARTICLE{Dolezal2022-qa,
    title    = "Uncertainty-informed deep learning models enable high-confidence
                predictions for digital histopathology",
    author   = "Dolezal, James M and Srisuwananukorn, Andrew and Karpeyev, Dmitry
                and Ramesh, Siddhi and Kochanny, Sara and Cody, Brittany and
                Mansfield, Aaron S and Rakshit, Sagar and Bansal, Radhika and
                Bois, Melanie C and Bungum, Aaron O and Schulte, Jefree J and
                Vokes, Everett E and Garassino, Marina Chiara and Husain, Aliya N
                and Pearson, Alexander T",
    journal  = "Nature Communications",
    volume   =  13,
    number   =  1,
    pages    = "6572",
    month    =  nov,
    year     =  2022
    }