DistalPhalanxOutlineAgeGroup

This series of 11 classification problems were created as part of Luke Davis's PhD titled "Predictive Modelling of Bone Ageing". They are all derived from the same images, extracted from Cao et al. "Digital hand atlas and web-based bone age assessment: system design and implementation". They are designed to test the efficacy of hand and bone outline detection and whether these outlines could be helpful in bone age prediction. Algorithms to automatically extract the hand outlines and then the outlines of three bones of the middle finger (proximal, middle and distal phalanges) were applied to over 1300 images, and three human evaluators labelled the output of the image outlining as correct or incorrect. This generated three classification problems: DistalPhalanxOutlineCorrect; MiddlePhalanxOutlineCorrect; and ProximalPhalanxOutlineCorrect. The next stage of the project was to use the outlines to predict information about the subjects age. The three problems {\em DistalPhalanxOutlineAgeGroup, MiddlePhalanxOutlineAgeGroup and ProximalPhalanxOutlineAgeGroup} involve using the outline of one of the phalanges to predict whether the subject is one of three age groups: 0-6 years old, 7-12 years old and 13-19 years old. Note that these problems are aligned by subject, and hence can be treated as a multi dimensional TSC problem. Problem Phalanges contains the concatenation of all three problems. Bone age estimation is usually performed by an expert with an algorithm called Tanner-Whitehouse. This involves scoring each bone into one of seven categories based on the stage of development. The final three bone image classification problems, DistalPhalanxTW, MiddlePhalanxTW and ProximalPhalanxTW}, involve predicting the Tanner-Whitehouse score (as labelled by a human expert) from the outline.

This is a pre-processed version of the dataset saved in numpy format. The original dataset is obtained from here.

The dataset consists of univariate time series associated with a class label. It can be loaded as follows:

loaded_data = np.load("PHAL.npz")
Xtr = loaded_data['Xtr'] # Training data of shape (400, 80)
Ytr = loaded_data['Ytr'] # Training labels of shape (400, 1)
Xte = loaded_data['Xte'] # Test data of shape (139, 80)
Yte = loaded_data['Yte'] # Test labels of shape (139, 1)