In the last decade, the research of artificial intelligence on medical images has attracted researchers' interest. One of the most popular directions is automated medical image segmentation (MIS) using deep learning, which aims to automatically assign labels to pixels so that the pixels with the same label from a segmented object. However, in the past years a strong trend of highlighting or cherry-picking improper metrics to show particularly high scores close to 100% was revealed in scientific publishing of MIS studies [1]. In addition, even though there are some papers that evaluate image segmentation results from different perspectives, the implementation of their evaluation algorithms is inconsistent. This is due to the lack of a universal metric library in Python for standardized and reproducible evaluation. Therefore, we propose to develop an open-source publicly available Python package seg-metrics, which aims to evaluate the performance of MIS models.

In supervised classification problems, the test set may contain data points belonging to classes not observed in the learning phase. Moreover, the same units in the test data may be measured on a set of additional variables recorded at a subsequent stage with respect to when the learning sample was collected. In this situation, the classifier built in the learning phase needs to adapt to handle potential unknown classes and the extra dimensions. We introduce a model-based discriminant approach, Dimension-Adaptive Mixture Discriminant Analysis (D-AMDA), which can detect unobserved classes and adapt to the increasing dimensionality. Model estimation is carried out via a full inductive approach based on an EM algorithm. The method is then embedded in a more general framework for adaptive variable selection and classification suitable for data of large dimensions. A simulation study and an artificial experiment related to classification of adulterated honey samples are used to validate the ability of the proposed framework to deal with complex situations.

Using H2O DeepLearning and a Tanh activation function, is it acceptable/valid to get a predicted (probability) value greater than 1? Details: I am using H2O for Deep Learning Artificial Neural Networks in R to predict 2 classes. My y data (actualResults) are the actual classifications of only 0s and 1s. The x independent variables are all numerical and the training frame excludes the y (actualResults). When I do a max on the predicted values, I get values greater than 1, never less than -1, though Tanh is suppose to be limited to [-1, 1]. Note: In the code below, the training_set and testing_set's first column is the actual classification, so -c(1) removes it for the network's input.