Month: January 2022

Posted by Abhijit Guha Roy, Research Software Engineer and Jie Ren, Research Scientist, Google Research

Deep machine learning (ML) systems have achieved considerable success in medical image analysis in recent years. One major contributing factor is access to abundant labeled datasets, which are used to train highly effective supervised deep learning models. However, in the real-world, these models may encounter samples exhibiting rare conditions that are individually too infrequent for per-condition classification. Nevertheless, such conditions can be collectively common because they follow a long-tail distribution and when taken together can represent a significant portion of cases — e.g., in a recent deep learning dermatological study, hundreds of rare conditions composed around 20% of cases encountered by the model at test time.

To prevent models from generating erroneous outputs on rare samples at test time, there remains a considerable need for deep learning systems with the ability to recognize when a sample is not a condition it can identify. Detecting previously unseen conditions can be thought of as an out-of-distribution (OOD) detection task. By successfully identifying OOD samples, preventive measures can be taken, like abstaining from prediction or deferring to a human expert.

Traditional computer vision OOD detection benchmarks work to detect dataset distribution shifts. For example, a model may be trained on CIFAR images but be presented with street view house numbers (SVHN) as OOD samples, two datasets with very different semantic meanings. Other benchmarks seek to detect slight differences in semantic information, e.g., between images of a truck and a pickup truck, or two different skin conditions. The semantic distribution shifts in such near-OOD detection problems are more subtle in comparison to dataset distribution shifts, and thus, are harder to detect.

In “Does Your Dermatology Classifier Know What it Doesn’t Know? Detecting the Long-Tail of Unseen Conditions”, published in Medical Image Analysis, we tackle this near-OOD detection task in the application of dermatology image classification. We propose a novel hierarchical outlier detection (HOD) loss, which leverages existing fine-grained labels of rare conditions from the long tail and modifies the loss function to group unseen conditions and improve identification of these near OOD categories. Coupled with various representation learning methods and the diverse ensemble strategy, this approach enables us to achieve better performance for detecting OOD inputs.

The Near-OOD Dermatology Dataset
We curated a near-OOD dermatology dataset that includes 26 inlier conditions, each of which are represented by at least 100 samples, and 199 rare conditions considered to be outliers. Outlier conditions can have as low as one sample per condition. The separation criteria between inlier and outlier conditions can be specified by the user. Here the cutoff sample size between inlier and outlier was 100, consistent with our previous study. The outliers are further split into training, validation, and test sets that are intentionally mutually exclusive to mimic real-world scenarios, where rare conditions shown during test time may have not been seen in training.

Long tail distribution of different dermatological conditions in our dataset. The 26 inlier conditions, with at least 100 samples, (blue) and the remaining 199 rare outlier conditions (orange). Outlier conditions can have as low as one sample per condition.

Hierarchical Outlier Detection Loss
We propose to use “known outlier” samples during training that are leveraged to aid detection of “unknown outlier” samples during test time. Our novel hierarchical outlier detection (HOD) loss performs a fine-grained classification of individual classes for all inlier or outlier classes and, in parallel, a coarse-grained binary classification of inliers vs. outliers in a hierarchical setup (see the figure below). Our experiments confirmed that HOD is more effective than performing a coarse-grained classification followed by a fine-grained classification, as this could result in a bottleneck that impacted the performance of the fine-grained classifier.

We use the sum of the predictive probabilities of the outlier classes as the OOD score. As a primary OOD detection metric we use the area under receiver operating characteristics (AUROC) curve, which ranges between 0 and 1 and gives us a measure of separability between inliers and outliers. A perfect OOD detector, which separates all inliers from outliers, is assigned an AUROC score of 1. A popular baseline method, called reject bucket, separates each inlier individually from the outliers, which are grouped into a dedicated single abstention class. In addition to a fine-grained classification for each individual inlier and outlier classes, the HOD loss–based approach separates the inliers collectively from the outliers with a coarse-grained prediction loss, resulting in better generalization. While similar, we demonstrate that our HOD loss–based approach outperforms other baseline methods that leverage outlier data during training, achieving an AUROC score of 79.4% on the benchmark, a significant improvement over that of reject bucket, which achieves 75.6%.

Our model architecture and the HOD loss. The encoder (green) represents the wide ResNet 101×3 model pre-trained with different representation learning models (ImageNet, BiT, SimCLR, and MICLe; see below). The output of the encoder is sent to the HOD loss where fine-grained and coarse-grained predictions for inliers (blue) and outliers (orange) are obtained. The coarse predictions are obtained by summing over the fine-grained probabilities as indicated in the figure. The OOD score is defined as the sum of the probabilities of outlier classes.

Representation Learning and the Diverse Ensemble Strategy
We also investigate how different types of representation learning help in OOD detection in conjunction with HOD by pretraining on ImageNet, BiT-L, SimCLR and MICLe models. We observe that including HOD loss improves OOD performance compared to the reject bucket baseline method for all four representation learning methods.

Another orthogonal approach for improving OOD detection performance and accuracy is deep ensemble, which aggregates outputs from multiple independently trained models to provide a final prediction. We build upon deep ensemble, but instead of using a fixed architecture with a fixed pre-training, we combine different representation learning architectures (ImageNet, BiT-L, SimCLR and MICLe) and introduce objective loss functions (HOD and reject bucket). We call this a diverse ensemble strategy, which we demonstrate outperforms the deep ensemble for OOD performance and inlier accuracy.

Downstream Clinical Trust Analysis
While we mainly focus on improving the performance for OOD detection, the ultimate goal for our dermatology model is to have high accuracy in predicting inlier and outlier conditions. We go beyond traditional performance metrics and introduce a “penalty” matrix that jointly evaluates inlier and outlier predictions for model trust analysis to approximate downstream impact. For a fixed confidence threshold, we count the following types of mistakes: (i) incorrect inlier predictions (i.e., mistaking inlier condition A as inlier condition B); (ii) incorrect abstention of inliers (i.e., abstaining from making a prediction for an inlier); and (iii) incorrect prediction for outliers as one of the inlier classes.

To account for the asymmetrical consequences of the different types of mistakes, penalties can be 0, 0.5, or 1. Both incorrect inlier and outlier-as-inlier predictions can potentially erode user trust in the model and were penalized with a score of 1. Incorrect abstention of an inlier as an outlier was penalized with a score of 0.5, indicating that potential model users should seek additional guidance given the model-expressed uncertainty or abstention. For correct decisions no cost is incurred, indicated by a score of 0.

Because real-world scenarios are more complex and contain a variety of unknown variables, the numbers used here represent simplifications to enable qualitative approximations for the downstream impact on user trust of outlier detection models, which we refer to as “cost”. We use the penalty matrix to estimate a downstream cost on the test set and compare our method against the baseline, thereby making a stronger case for its effectiveness in real-world scenarios. As shown in the plot below, our proposed solution incurs a much lower estimated cost in comparison to baseline over all possible operating points.

Trust analysis comparing our proposed method to the baseline (reject bucket) for a range of outlier recall rates, indicated by 𝛕. We show that our method reduces downstream estimated cost, potentially reflecting improved downstream impact.

Conclusion
In real-world deployment, medical ML models may encounter conditions that were not seen in training, and it’s important that they accurately identify when they do not know a specific condition. Detecting those OOD inputs is an important step to improving safety. We develop an HOD loss that leverages outlier data during training, and combine it with pre-trained representation learning models and a diverse ensemble to further boost performance, significantly outperforming the baseline approach on our new dermatology benchmark dataset. We believe that our approach, aligned with our AI Principles, can aid successful translation of ML algorithms into real-world scenarios. Although we have primarily focused on OOD detection for dermatology, most of our contributions are fairly generic and can be easily incorporated into OOD detection for other applications.

Acknowledgements
We would like to thank Shekoofeh Azizi, Aaron Loh, Vivek Natarajan, Basil Mustafa, Nick Pawlowski, Jan Freyberg, Yuan Liu, Zach Beaver, Nam Vo, Peggy Bui, Samantha Winter, Patricia MacWilliams, Greg S. Corrado, Umesh Telang, Yun Liu, Taylan Cemgil, Alan Karthikesalingam, Balaji Lakshminarayanan, and Jim Winkens for their contributions. We would also like to thank Tom Small for creating the post animation.

Posted by Matt McEwen, Student Researcher, Google Quantum AI and Lara Faoro, Research Fellow, LPTHE- Sorbonne Université and CNRS (Paris)

Quantum processors are made of superconducting quantum bits (qubits) that — being quantum objects — are highly susceptible to even tiny amounts of environmental noise. This noise can cause errors in quantum computation that need to be addressed to continue advancing quantum computers. Our Sycamore processors are installed in specially designed cryostats, where they are sealed away from stray light and electromagnetic fields and are cooled down to very low temperatures to reduce thermal noise.

However, the world is full of high-energy radiation. In fact, there’s a tiny background of high-energy gamma rays and muons that pass through everything around us all the time. While these particles interact so weakly that they don’t cause any harm in our day-to-day lives, qubits are sensitive enough that even weak particle interactions can cause significant interference.

In “Resolving Catastrophic Error Bursts from Cosmic Rays in Large Arrays of Superconducting Qubits”, published in Nature Physics, we identify the effects of these high-energy particles when they impact the quantum processor. To detect and study individual impact events, we use new techniques in rapid, repetitive measurement to operate our processor like a particle detector. This allows us to characterize the resulting burst of errors as they spread through the chip, helping to better understand this important source of correlated errors.

The Dynamics of a High-Energy Impact
The Sycamore quantum processor is constructed with a very thin layer of superconducting aluminum on a silicon substrate, onto which a pattern is etched to define the qubits. At the center of each qubit is the Josephson junction, a superconducting component that defines the distinct energy levels of the qubit, which are used for computation. In a superconducting metal, electrons bind together into a macroscopic, quantum state, which allows electrons to flow as a current with zero resistance (a supercurrent). In superconducting qubits, information is encoded in different patterns of oscillating supercurrent going back and forth through the Josephson junction.

If enough energy is added to the system, the superconducting state can be broken up to produce quasiparticles. These quasiparticles are a problem, as they can absorb energy from the oscillating supercurrent and jump across the Josephson junction, which changes the qubit state and produces errors. To prevent any energy from being absorbed by the chip and producing quasiparticles, we use extensive shielding for electric and magnetic fields, and powerful cryogenic refrigerators to keep the chip near absolute zero temperature, thus minimizing the thermal energy.

A source of energy that we can’t effectively shield against is high-energy radiation, which includes charged particles and photons that can pass straight through most materials. One source of these particles are tiny amounts of radioactive elements that can be found everywhere, e.g., in building materials, the metal that makes up our cryostats, and even in the air. Another source is cosmic rays, which are extremely energetic particles produced by supernovae and black holes. When cosmic rays impact the upper atmosphere, they create a shower of high-energy particles that can travel all the way down to the surface and through our chip. Between radioactive impurities and cosmic ray showers, we expect a high energy particle to pass through a quantum chip every few seconds.

When a high-energy impact event occurs, energy spreads through the chip in the form of phonons. When these arrive at the superconducting qubit layer, they break up the superconducting state and produce quasiparticles, which cause the qubit errors we observe.

When one of these particles impinges on the chip, it passes straight through and deposits a small amount of its energy along its path through the substrate. Even a small amount of energy from these particles is a very large amount of energy for the qubits. Regardless of where the impact occurs, the energy quickly spreads throughout the entire chip through quantum vibrations called phonons. When these phonons hit the aluminum layer that makes up the qubits, they have more than enough energy to break the superconducting state and produce quasiparticles. So many quasiparticles are produced that the probability of the qubits interacting with one becomes very high. We see this as a sudden and significant increase in errors over the whole chip as those quasiparticles absorb energy from the qubits. Eventually, as phonons escape and the chip cools, these quasiparticles recombine back into the superconducting state, and the qubit error rates slowly return to normal.

A high-energy particle impact (at time = 0 ms) on a patch of the quantum processor, showing error rates for each qubit over time. The event starts by rapidly spreading error over the whole chip, before saturating and then slowly returning to equilibrium.

Detecting Particles with a Computer
The Sycamore processor is designed to perform quantum error correction (QEC) to improve the error rates and enable it to execute a variety of quantum algorithms. QEC provides an effective way of identifying and mitigating errors, provided they are sufficiently rare and independent. However, in the case of a high-energy particle going through the chip, all of the qubits will experience high error rates until the event cools off, producing a correlated error burst that QEC won’t be able to correct. In order to successfully perform QEC, we first have to understand what these impact events look like on the processor, which requires operating it like a particle detector.

To do so, we take advantage of recent advances in qubit state preparation and measurement to quickly prepare each qubit in their excited state, similar to flipping a classical bit from 0 to 1. We then wait for a short idle time and measure whether they are still excited. If the qubits are behaving normally, almost all of them will be. Further, the qubits that experience a decay out of their excited state won’t be correlated, meaning the qubits that have errors will be randomly distributed over the chip.

However, during the experiment we occasionally observe large error bursts, where all the qubits on the chip suddenly become more error prone all at once. This correlated error burst is a clear signature of a high-energy impact event. We also see that, while all qubits on the chip are affected by the event, the qubits with the highest error rates are all concentrated in a “hotspot” around the impact site, where slightly more energy is deposited into the qubit layer by the spreading phonons.

To detect high-energy impacts, we rapidly prepare the qubits in an excited state, wait a little time, and then check if they’ve maintained their state. An impact produces a correlated error burst, where all the qubits show a significantly elevated error rate, as shown around time = 8 seconds above.

Next Steps
Because these error bursts are severe and quickly cover the whole chip, they are a type of correlated error that QEC is unable to correct. Therefore, it’s very important to find a solution to mitigate these events in future processors that are expected to rely on QEC.

Shielding against these particles is very difficult and typically requires careful engineering and design of the cryostat and many meters of shielding, which becomes more impractical as processors grow in size. Another approach is to modify the chip, allowing it to tolerate impacts without causing widespread correlated errors. This is an approach taken in other complex superconducting devices like detectors for astronomical telescopes, where it’s not possible to use shielding. Examples of such mitigation strategies include adding additional metal layers to the chip to absorb phonons and prevent them from getting to the qubit, adding barriers in the chip to prevent phonons spreading over long distances, and adding traps for quasiparticles in the qubits themselves. By employing these techniques, future processors will be much more robust to these high-energy impact events.

As the error rates of quantum processors continue to decrease, and as we make progress in building a prototype of an error-corrected logical qubit, we’re increasingly pushed to study more exotic sources of error. While QEC is a powerful tool for correcting many kinds of errors, understanding and correcting more difficult sources of correlated errors will become increasingly important. We’re looking forward to future processor designs that can handle high energy impacts and enable the first experimental demonstrations of working quantum error correction.

Acknowledgements
This work wouldn’t have been possible without the contributions of the entire Google Quantum AI Team, especially those who worked to design, fabricate, install and calibrate the Sycamore processors used for this experiment. Special thanks to Rami Barends and Lev Ioffe, who led this project.