These data sets were originally created for the following publications: M. E. Houle, H.-P. Kriegel, P. Kröger, E. Schubert, A. Zimek Can Shared-Neighbor Distances Defeat the Curse of Dimensionality? In Proceedings of the 22nd International Conference on Scientific and Statistical Database Management (SSDBM), Heidelberg, Germany, 2010. H.-P. Kriegel, E. Schubert, A. Zimek Evaluation of Multiple Clustering Solutions In 2nd MultiClust Workshop: Discovering, Summarizing and Using Multiple Clusterings Held in Conjunction with ECML PKDD 2011, Athens, Greece, 2011. The outlier data set versions were introduced in: E. Schubert, R. Wojdanowski, A. Zimek, H.-P. Kriegel On Evaluation of Outlier Rankings and Outlier Scores In Proceedings of the 12th SIAM International Conference on Data Mining (SDM), Anaheim, CA, 2012. They are derived from the original image data available at https://aloi.science.uva.nl/ The image acquisition process is documented in the original ALOI work: J. M. Geusebroek, G. J. Burghouts, and A. W. M. Smeulders, The Amsterdam library of object images, Int. J. Comput. Vision, 61(1), 103-112, January, 2005 Additional information is available at: https://elki-project.github.io/datasets/multi_view The following views are currently available: Feature type Description Files Object number Sparse 1000 dimensional vectors that give the true object assignment objs.arff.gz RGB color histograms Standard RGB color histograms (uniform binning) aloi-8d.csv.gz aloi-27d.csv.gz aloi-64d.csv.gz aloi-125d.csv.gz aloi-216d.csv.gz aloi-343d.csv.gz aloi-512d.csv.gz aloi-729d.csv.gz aloi-1000d.csv.gz HSV color histograms Standard HSV/HSB color histograms in various binnings aloi-hsb-2x2x2.csv.gz aloi-hsb-3x3x3.csv.gz aloi-hsb-4x4x4.csv.gz aloi-hsb-5x5x5.csv.gz aloi-hsb-6x6x6.csv.gz aloi-hsb-7x7x7.csv.gz aloi-hsb-7x2x2.csv.gz aloi-hsb-7x3x3.csv.gz aloi-hsb-14x3x3.csv.gz aloi-hsb-8x4x4.csv.gz aloi-hsb-9x5x5.csv.gz aloi-hsb-13x4x4.csv.gz aloi-hsb-14x5x5.csv.gz aloi-hsb-10x6x6.csv.gz aloi-hsb-14x6x6.csv.gz Color similiarity Average similarity to 77 reference colors (not histograms) 18 colors x 2 sat x 2 bri + 5 grey values (incl. white, black) aloi-colorsim77.arff.gz (feature subsets are meaningful here, as these features are computed independently of each other) Haralick features First 13 Haralick features (radius 1 pixel) aloi-haralick-1.csv.gz Front to back Vectors representing front face vs. back faces of individual objects front.arff.gz Basic light Vectors indicating basic light situations light.arff.gz Manual annotations Manually annotated object groups of semantically related objects such as cups manual1.arff.gz Outlier Detection Versions Additionally, we generated a number of subsets for outlier detection: Feature type Description Files RGB Histograms Downsampled to 100000 objects (553 outliers) aloi-27d-100000-max10-tot553.csv.gz aloi-64d-100000-max10-tot553.csv.gz Downsampled to 75000 objects (717 outliers) aloi-27d-75000-max4-tot717.csv.gz aloi-64d-75000-max4-tot717.csv.gz Downsampled to 50000 objects (1508 outliers) aloi-27d-50000-max5-tot1508.csv.gz aloi-64d-50000-max5-tot1508.csv.gz

IntroductionCreating training and testing datasets for machine learning algorithms to measure linear dimensions of organs is a tedious task. There are no universally accepted methods for evaluating outliers or anomalies in such datasets. This can cause errors in machine learning and compromise the quality of end products. The goal of this study is to identify optimal methods for detecting organ anomalies and outliers in medical datasets designed to train and test neural networks in morphometrics.MethodsA dataset was created containing linear measurements of the spleen obtained from CT scans. Labelling was performed by three radiologists. The total number of studies included in the sample was N = 197 patients. Using visual methods (1.5 interquartile range; heat map; boxplot; histogram; scatter plot), machine learning algorithms (Isolation forest; Density-Based Spatial Clustering of Applications with Noise; K-nearest neighbors algorithm; Local outlier factor; One-class support vector machines; EllipticEnvelope; Autoencoders), and mathematical statistics (z-score, Grubb’s test; Rosner’s test).ResultsWe identified measurement errors, input errors, abnormal size values and non-standard shapes of the organ (sickle-shaped, round, triangular, additional lobules). The most effective methods included visual techniques (including boxplots and histograms) and machine learning algorithms such is OSVM, KNN and autoencoders. A total of 32 outlier anomalies were found.DiscussionCuration of complex morphometric datasets must involve thorough mathematical and clinical analyses. Relying solely on mathematical statistics or machine learning methods appears inadequate.

The AICr column reports the average L1 distance while other reports the relative L1 distances compared to AICr.

The AICr column reports the average Hellinger distance while other reports the relative Hellinger distances compared to AICr.

Values of 0.935* and 0.779** after removal of two outliers.