Classification of melanoma tumor images. The data is a preprocessed version of the 2020 SIIM-ISIC challenge where the images have been reshaped to size $(3, 128, 128)$.
By default only the training rows are active in the task,
but the test data (that has no targets) is also included.
Whether an observation is part of the train or test set is indicated by the column "test".
There are no labels for the test rows, so by default, these observations are inactive, which means that the task uses only 32701 of the 43683 observations that are defined in the underlying data backend.
The data backend also contains a more detailed diagnosis of the specific type of tumor.
Columns:
outcome(factor): the target variable. Whether the tumor is benign or malignant (the positive class)anatom_site_general_challenge(factor): the location of the tumor on the patient's bodysex(factor): the sex of the patientage_approx(int): approximate age of the patient at the time of imagingimage(lazy_tensor): The image (shape $(3, 128, 128)$) of the tumor. eesplit(character): Whether the observation os part of the train or test set.
Download
The task's backend is a DataBackendLazy which will download the data once it is requested.
Other meta-data is already available before that.
You can cache these datasets by setting the mlr3torch.cache option to TRUE or to a specific path to be used
as the cache directory.
Properties
Task type: “classif”
Properties: “twoclass”, “groups”
Has Missings: no
Target: “outcome”
Features: “sex”, “anatom_site_general_challenge”, “age_approx”, “image”
Data Dimension: 43683x11
References
Rotemberg, V., Kurtansky, N., Betz-Stablein, B., Caffery, L., Chousakos, E., Codella, N., Combalia, M., Dusza, S., Guitera, P., Gutman, D., Halpern, A., Helba, B., Kittler, H., Kose, K., Langer, S., Lioprys, K., Malvehy, J., Musthaq, S., Nanda, J., Reiter, O., Shih, G., Stratigos, A., Tschandl, P., Weber, J., Soyer, P. (2021). “A patient-centric dataset of images and metadata for identifying melanomas using clinical context.” Scientific Data, 8, 34. doi:10.1038/s41597-021-00815-z .
Examples
task = tsk("melanoma")
task
#> <TaskClassif:melanoma> (32701 x 5): Melanoma Classification
#> * Target: outcome
#> * Properties: twoclass, groups
#> * Features (4):
#> - fct (2): anatom_site_general_challenge, sex
#> - int (1): age_approx
#> - lt (1): image
#> * Groups: patient_id