Injecting and removing malignant features in mammography with CycleGAN: Investigation of an automated adversarial attack using neural networks

11/19/2018
by   Anton S. Becker, et al.
16

Purpose To train a cycle-consistent generative adversarial network (CycleGAN) on mammographic data to inject or remove features of malignancy, and to determine whether these AI-mediated attacks can be detected by radiologists. Material and Methods From the two publicly available datasets, BCDR and INbreast, we selected images from cancer patients and healthy controls. An internal dataset served as test data, withheld during training. We ran two experiments training CycleGAN on low and higher resolution images (256 × 256 px and 512 × 408 px). Three radiologists read the images and rated the likelihood of malignancy on a scale from 1-5 and the likelihood of the image being manipulated. The readout was evaluated by ROC analysis (Area under the ROC curve = AUC). Results At the lower resolution, only one radiologist exhibited markedly lower detection of cancer (AUC=0.85 vs 0.63, p=0.06), while the other two were unaffected (0.67 vs. 0.69 and 0.75 vs. 0.77, p=0.55). Only one radiologist could discriminate between original and modified images slightly better than guessing/chance (0.66, p=0.008). At the higher resolution, all radiologists showed significantly lower detection rate of cancer in the modified images (0.77-0.84 vs. 0.59-0.69, p=0.008), however, they were now able to reliably detect modified images due to better visibility of artifacts (0.92, 0.92 and 0.97). Conclusion A CycleGAN can implicitly learn malignant features and inject or remove them so that a substantial proportion of small mammographic images would consequently be misdiagnosed. At higher resolutions, however, the method is currently limited and has a clear trade-off between manipulation of images and introduction of artifacts.

READ FULL TEXT
research
03/29/2023

Problems and shortcuts in deep learning for screening mammography

This work reveals undiscovered challenges in the performance and general...
research
09/24/2021

Identifying Women with Mammographically-Occult Breast Cancer Leveraging GAN-Simulated Mammograms

Our objective is to show the feasibility of using simulated mammograms t...
research
05/30/2020

Advanced Single Image Resolution Upsurging Using a Generative Adversarial Network

The resolution of an image is a very important criterion for evaluating ...
research
03/01/2021

Noncoding RNAs and deep learning neural network discriminate multi-cancer types

Detecting cancers at early stages can dramatically reduce mortality rate...
research
03/26/2023

Biologically-primed deep neural network improves colorectal Cancer Molecular subtypes prediction from H E stained images

Colorectal cancer (CRC) molecular subtypes play a crucial role in determ...

Please sign up or login with your details

Forgot password? Click here to reset