thyroid-brs-gan-v1

Thyroid BRAF-RAS Score (BRS) GAN v1 Model Card

This model card describes a model associated with a manuscript that is currently under review. Links to the manuscript will be provided once publicly available.

Model Details

• Developed by: James Dolezal
• Model type: Generative adversarial network
• Language(s): English
• License: GPL-3.0
• Model Description: This is a StyleGAN2 model that can generate synthetic H&E pathologic images of thyroid neoplasms. The GAN is conditioned on discretized BRAF-RAS Score (BRS), a a gene expression score scaled from -1 (BRAF-like) to +1 (RAS-like) indicating how similar a tumor's gene expression is to a BRAF-mutant and RAS-mutant tumor. The GAN has been conditioned on the categories BRAF-like (=0) and RAS-like (=1).
• Image processing: This model generates images at 512 x 512 px resolution and was trained on lossless (PNG) pathologic images at 302 x 302 μm magnification.
• Resources for more information: GitHub Repository

Uses

Examples

This model is a StyleGAN2 model and can be used with any StyleGAN-compatible scripts and tools. The GitHub repository associated with his model includes detailed information on how to interface with the GAN, generate images, and perform class blending via embedding interpolation.

Direct Use

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

• Applications in educational settings.
• Research on pathology classification models for thyroid neoplasms.

Excluded uses are described below.

Misuse and Out-of-Scope Use

Output from this model should not be used in a clinical setting or be provided to 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 influencing a patient's health care treatment in any way based on output from this model.

Limitations

The model has not been validated in contexts where non-thyroid neoplasms, or rare thyroid subtypes such as anaplastic thyroid carcinoma, are possible.

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).

Training

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

• The Cancer Genome Atlas (TCGA), THCA cohort (see next section)

This model was trained on a total of 369 slides, with 116 BRAF-like tumors and 271 RAS-like tumors.

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 512 x 512 px using Libvips. During training, images are randomly flipped and rotated (90, 180, 270). Training is otherwise identical to the official StyleGAN2 implementation.

Additional training information:

• Hardware: 2 x A100 GPUs
• Batch size: 16
• R1 gamma: 3.2768
• Training time: 12,720 kimg

Evaluation Results

External evaluation results are currently under peer review and will be posted once publicly available.