Medical Out-of-Distribution Analysis Challenge 2023
Creators: name: Zimmerer, David
name: Köhler, Gregor
name: Jäger, Paul
name: Full, Peter
name: Maier-Hein, Klaus
name: Roß, Tobias
name: Adler, Tim
name: Reinke, Annika
name: Maier-Hein, Lena
Publisher: name: Zenodo
Year: 2023
Subjects: Anomaly detection
Anomaly localization
Out-of-distribution detection
novelty detection
MICCAI Challenges

description: Despite overwhelming successes in recent years, progress in the field of biomedical image computing still largely depends on the availability of annotated training examples. This annotation process is often prohibitively expensive because it requires the valuable time of domain experts. Additionally, this approach simply does not scale well: whenever a new imaging modality is created, acquisition parameters change. Even something as basic as the target demographic is prone to changes, and new annotated cases have to be created to allow methods to cope with the resulting images. Image labeling is thus bound to become the major bottleneck in the coming years. Furthermore, it has been shown that many algorithms used in image analysis are vulnerable to out-of-distribution samples, resulting in wrong and overconfident decisions [20, 21, 22, 23]. In addition, physicians can overlook unexpected conditions in medical images, often termed ‘inattentional blindness’. In [1], Drew et al. noted that 50% of trained radiologists did not notice a gorilla image, rendered into a lung CT scan when assessing lung nodules. One approach, which does not require labeled images and can generalize to unseen pathological conditions, is Out-of-Distribution or anomaly detection (which in this context is used interchangeably). Anomaly detection can recognize and outline conditions that have not been previously encountered during training and thus circumvents the time-consuming labeling process and can therefore quickly be adapted to new modalities. Additionally, by highlighting such abnormal regions, anomaly detection can guide the physicians’ attention to otherwise overlooked abnormalities in a scan and potentially improve the time required to inspect medical images. However, while there is a lot of recent research on improving anomaly detection [8, 9, 10, 11, 12, 13, 14, 15, 16, 17], especially with a focus on the medical field [4, 5, 6, 7], a common dataset/ benchmark to compare different approaches is missing. Thus, it is currently hard to have a fair comparison of different proposed approaches. While in the last few months common datasets for natural data were proposed, such as default detection [3] or abnormal traffic scene detection [2], we tried to tackle this issue for medical imaging with last year's challenge [25]. In a similar setting to the last years, we suggest the medical out-of-distribution challenge as a standardized dataset and benchmark for anomaly detection. We propose two different tasks. First a sample-wise (i.e. patients-wise) analysis, thus detecting out-of-distribution samples. For example, having a pathological condition or any other condition not seen in the training-set. This can pose a problem to classically supervised algorithms and detection of such could further allow physicians to prioritize different patients. Secondly, we propose a voxel-wise analysis i.e. giving a score for each voxel, highlighting abnormal conditions, and potentially guiding the physician. However, there are a few aspects to consider when choosing an anomaly detection dataset. First, as in reality, the types of anomalies should not be known beforehand. This can be a particular problem when choosing a dataset and testing on only a single pathological condition, which is vulnerable to exploitation. Even with an educated guess (based on the dataset) and a fully supervised segmentation approach, trained on a not allowed separate dataset, one could outperform other rightfully trained anomaly detection approaches. Furthermore, making the exact types of anomalies known can cause a bias in the evaluation. Studies have shown that proposed anomaly detection algorithms tend to overfit on a given task, given that properties of the test set and the kind of anomalies are known beforehand. This further hinders the comparability of different algorithms [6, 18, 19, 23]. As a second point, combining test sets, from different sources with alternative conditions, may also cause problems. By definition, the different sources already propose a distribution shift to the training dataset, complicating a clean and meaningful evaluation. To solve these issues we propose to provide two datasets with more than 600 scans each, one brain MRI-dataset and one abdominal CT-dataset, to allow for a comparison of the generalizability of the approaches. In order to prevent overfitting on the (types of) anomalies existing in our test set, the test set will be kept confidential at all times. The training set consists of hand-selected scans in which no anomalies were identified. The remaining scans will be assigned to the test set. Thus some scans in the test set do not contain anomalies, whilst others contain naturally occurring anomalies. In addition to the natural anomalies, we will add synthetic anomalies. We choose different structured types of synthetic anomalies (e.g. a tumor or an image of a gorilla rendered into the a brain scan [1]) to cover a broad variety of different anomalies and also allow for an analysis of weaknesses and strengths of the methods by different factors (type, size, contrast, ...). We believe that this allows for a controlled and fair comparison of different algorithms (as recently similarly proposed by [3]). While in the 2020 edition [25] multiple different approaches were present, among which some showed clearly superior performance for certain kinds of anomalies, generally all algorithms still failed in (different) obvious cases and especially medically relevant classes of anomalies where still lacking in (clinically relevant) performance. In last year's edition we introduced new synthetic anomalies and furthermore focused more on clinically relevant and not very well performing anomalies from the 2020 challenge. This has led to a more equal performance between the datasets and improvements in the medical conditions class which overall shows a more promising of the approaches. However, the total performance is still far from medical relevancy and has large potential for improvements. As such we believe that the current set of anomalies allows for great methological improvements. In the last editions, we used the AP metric for both tasks, the sample-level task and the pixel-level task. While for the sample-level task this is quite established and was recommended by multiple other researchers [9], the medical community (due to the lack of a better or more established metrc) used the AP for the pixel-level task as well. In<br> discussions with our clinical partners, in a recent real-world evaluation of anomaly detection approaches [26] as well as in last years edition we found, however, that a more object-centric metric (similar to object detection) might be more aligned with the medical objective. Consequently, we want to keep the categories the same, renew<br> some synthetic samples and present a continuation to the challenge from last year but with a more object-centric metric for the pixel-level task. We hope that providing a such standardized dataset allows for a fair comparison of different approaches and can outline how well different approaches work in a realistic and clinical setting. [1] Drew, Trafton, Melissa L. H. Vo, and Jeremy M. 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