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Rafael López González

Abstract Hacking Codes. Hacker Computer Screen Closeup

Hacking Medical Images:
Fighting AI with AI

Recently, a new study led by Ben-Gurion University (Israel) in which AI was used to attack medical centers was published. The authors claimed they had developed a Deep Learning algorithm that was able to add or remove lung cancer from CT scans. The software was tested by attempting to mislead the diagnoses of three radiologists with 2, 5 and 7 years of experience, achieving an average success rate of 99.2% for cancer addition and 95.8% for cancer removal. These are alarming results, as the study proved that such attacks could be very harmful for medical institutions and their patients if somebody with bad intentions would use them. Therefore, designing countermeasures to guarantee the integrity of the medical data and protect health centers from this potential threat is a must for medical informatics departments. Below, we unveil what’s behind these AI cyberattacks and how they can be prevented and disarmed.

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The attack

The cyberattack that was proposed in the study was based in the use of Generative Adversarial Networks (GAN), a state-of-the-art Deep Learning technique introduced in 2014 by Ian Goodfellow, one of the most reputed AI researchers across the globe. The main idea behind this method lays in training two neural networks, called generator and discriminator, whose purpose is to defeat each other. On one hand, the objective of the generator network is to create fake data, that must be as realistic as possible. On the other hand, the goal of the discriminator network is to distinguish real data from the fake data created by the generator network. Both networks improve iteratively, as in each stage of the training the generator network is better at falsifying data and the discriminator is better at distinguishing false data, forcing each other to get better.

For the particular application of adding and removing lung cancer from CT studies there were two GAN models trained, one for the addition of lung cancer and another one for the removal and, therefore, there was a specialized generator network for the injection of fake tumors and another one specialized on removing them.



The defense

The first line of defense of medical centers should be handled by cybersecurity experts, designing communication networks robust to any kind of intrusion. But at QUIBIM we believe that even though cybersecurity is essential, there should be one more layer of protection against this kind of malware. We propose to defeat AI using AI if the hospital network is compromised. As explained before, GAN approaches consist of two networks. In the attack proposed in the study mentioned previously, the researchers developed a generator model able to misguide clinical decisions. Our defense approach is based on the opposite, that is, the design and implementation of an excellent discriminator network able to surpass the generator model and, therefore, being able to detect the artificially generated images. This network purpose would be to learn to look beyond what a human eye can detect, which would make a perfect barrier against generative attacks.

Some final words

In conclusion, Ben-Gurion University publication helped to highlight that even though AI unlocked many new opportunities and allowed major breakthroughs in the medical sector, it also favored the development of potentially harmful applications. Hence, there is a need for medical centers to build a strong AI strategy, not only to improve patient care, but to be prepared and protected against AI malware. Because, as all revolutionary technologies, AI can be used for the best, but for the worst as well.

At QUIBIM, our mission is to improve humans’ health by applying advanced and innovative image processing techniques to radiological images, and we guarantee that we will make use of our extended expertise and advanced algorithms to be prepared against any malignant software that aims to mislead the results of our analysis pipelines or our customers’ clinical outcomes.


New Chest X-Ray Classification Tool

Despite the technological evolution of imaging modalities like CT, US and MRI, conventional X-ray remains the most performed examination in radiology departments, and remains a fundamental tool for anatomical analysis in the detection and diagnosis of respiratory diseases and bone tissue alterations. However, radiology departments have limitations in reporting the X-Rays due to the limited resources available (link).

QUIBIM has developed a Chest X-Ray Classification Tool that offers a solution to this problem which can help radiology departments become even more efficient. This classifier developed in collaboration with Hospital Universitario y Politécnico La Fe, estimates the probability of chest X-Rays of having a pathology using Artificial Intelligence.

How does it work?

This tool makes use of fourteen Convolutional Neural Networks trained with a database of more than 100,000 images (NIH ChestXray14 dataset) to estimate the probability of presence of the following pathologies in chest X-Rays: atelectasis, cardiomegaly, effusion, infiltration, mass, nodule, pneumonia, pneumothorax, consolidation, edema, emphysema, fibrosis, pleural thickening and hernia. Afterwards, the probabilities are used by a Fully Connected Neural Network to get the final probability of the X-Ray of being abnormal.

XRAY chest

Because of this AI methodology the classifier understands the visual patterns that are most indicative of the different pathologies using the knowledge extracted from the large dataset of radiographs used to train the networks. QUIBIM’s Chest X-Ray Classification Tool is able to learn further using new images, which means that this system is continually improving and evolving with time.

 What information does it provide?

Once all the quantifications are performed this tool provides an intuitive Structured Report with the patient’s information, the abnormal probability of the radiographs analyzed and the representation of the findings. In addition, if the image is classified as abnormal, the report shows the three pathologies that are more likely to be found and a heatmap that highlights the most abnormal regions. This report is designed to be very user friendly, to assist the user in understanding the tool’s findings at a glance in order to make its usage highly efficient.

Why is this new tool so useful?

Using this technology it is possible to prioritize unreported, potentially pathological radiographs which allows radiologists to focus their efforts on studies that are more likely to have pathologies and thereby become more efficient. This tool essentially ensures that pathological findings, which could have been unreported due to the heavy workload of radiology departments, are correctly reported.

QUIBIM’s goal is to provide the radiology departments with an optimal solution for automatic reading of X-Rays without interfering in the  workflow of the department.

QUIBIM’S Chest X-Ray Analysis Tool is already available at QUIBIM Precision® Depending on the needs of the department, this tool is accessible through the cloud with just a few clicks  or  it can be fully integrated in the radiology department’s workflow as a local solution for  seamless interpretation of  chest X-Rays.

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