Brief Review — Conditional Generative Adversarial and Convolutional Networks for X-ray Breast Mass Segmentation and Shape Classification
cGAN+Autoencoder, or cGAN+U-Net
Conditional Generative Adversarial and Convolutional Networks for X-ray Breast Mass Segmentation and Shape Classification,
cGAN-AutoEnc & cGAN-Unet, by Bioinformatics Institute, Kayakalp Hospital, and Hospital Universitari Sant Joan de Reus
2018 MICCAI, Over 40 Citations (Sik-Ho Tsang @ Medium)
Medical Image Analysis, Image Segmentation, Image Classification
- Conditional Generative Adversarial Networks (cGAN) for breast mass segmentation in mammography. Autoencoder or U-Net can be used as backbone.
- After segmentation, another CNN is used for shape classification.
1. Breast Mass Segmentation Using cGAN
- The Generator network G of the cGAN is an FCN network composed of two networks: encoders and decoders.
- It can be Autoencoder and U-Net. It outputs the binary mask, in which each entry is the two output classes (mass/normal).
- The Discriminative network D is to classify the if the binary mask is generated by G or real.
- Let x represents a mass ROI image, y the corresponding ground truth segmentation, z a random variable, G(x, z) is the predicted mask, ||y−G(x, z)||1 is the L1 normalized distance between ground truth and predicted masks, λ is an empirical weighting factor and D(x, G(x, z)) is the output score of the discriminator, the generator loss is defined as:
- The discriminator loss is:
- This combination of generator/discriminator networks allows robust learning with very few training samples.
- A post-processing morphological filtering (i.e., erosion and dilation) is used to remove the artifacts and small white regions from the binary masks.
2. Classification Using CNN
- The input images for this stage (binary masks) do not render complex distribution of pixel values, just morphological structure, a simple CNN (i.e., two convolutional layers plus two fully connected layers) is used to learn a generalization of the four mass shapes.
- The cGAN-Unet provides the best results of all computed metrics on the DDSM test samples, with very remarkable Accuracy, Dice and Jaccard scores (around 97%, 94% and 89%, respectively).
- On the in-house private dataset, however, the cGAN-AutoEnc yields better results than the cGAN-Unet in terms of Dice, Jaccard and Sensitivity (+2%, +4% and +12%, respectively), which indicates that the cGAN-AutoEnc has learned a more generalized representation of tumor features since it performs better on the dataset not used for training.
- For the shape classification, the proposed CNN obtains overall accuracy around 72%.
3.3. Relationship Between Shapes and Subtypes
- Tumor shape could play an important role to predict the breast cancer molecular subtypes .
- Luminal-A and -B groups are mostly assigned to irregular and lobular shape classes.
- In turn, oval and round masses give indications to the Her-2 and Basal-like groups.
[2018 MICCAI] [cGAN-AutoEnc & cGAN-Unet]
Conditional Generative Adversarial and Convolutional Networks for X-ray Breast Mass Segmentation and Shape Classification
1.11. Biomedical Multi-Task Learning
2018 [ResNet+Mask R-CNN] [cU-Net+PE] [Multi-Task Deep U-Net] [cGAN-AutoEnc & cGAN-Unet]