Separation-survivability - the elusive moral cut-off point?

LetterThe original publication is available at http://www.samj.org.za[No abstract available]Publisher’s versio

Prognostic implications of mean nuclear diameter in breast cancer.

The mean nuclear diameter of 100 breast cancers was measured on tissue sections, to evaluate its importance for early prognosis. The cases were subdivided into 3 subgroups: small (25.5% of cases), medium (63.3%) and large (11.2%) nuclei. Early recurrence and mortality rates were investigated in each of the categories. Increasing nuclear size was shown to be related to mortality from metastatic disease. However, large-nucleus tumours had an inverse relationship with lymphnode involvement and possibly with recurrence rate. Hence, in our material nuclear size as a sole criterion was not a good indicator of the early behaviour of operable breast cancer

Comparative study of deep learning methods for one-shot image classification (abstract)

Training deep learning models for images classification requires large amount of labeled data to overcome the challenges of overfitting and underfitting. Usually, in many practical applications, these labeled data are not available. In an attempt to solve this problem, the one-shot learning paradigm tries to create machine learning models capable to learn well from one or (maximum) few labeled examples per class. To understand better the behavior of various deep learning models and approaches for one-shot learning, in this abstract, we perform a comparative study of the most used ones, on a challenging real-world dataset, i.e Fashion-MNIST