Cascade learning with abstention and individualised feature selection is a class of models in
high demand in personalised medical applications. The cascade consists of sequential …

We propose a method to train a cascade of classifiers by simultaneously optimizing all its
stages. The approach relies on the idea of optimizing soft cascades. In particular, instead of …

In many prediction tasks such as medical diagnostics, sequential decisions are crucial to
provide optimal individual treatment. Budget in real-life applications is always limited, and it …

The supervised learning in bioinformatics is a major tool to diagnose a disease, to identify
the best therapeutic strategy or to establish a prognostic. The main objective in classifier …

Cascade Learning (CL) is a new and alternative form of training a deep neural network in a
layer-wise fashion. This varied training strategy results in different feature representations …

Cascades are a classical strategy to enable inference cost to vary adaptively across
samples, wherein a sequence of classifiers are invoked in turn. A deferral rule determines …

A cascade of classifiers, working within a detection procedure, extracts and uses different
number of features depending on the window under analysis. Windows with background …

We develop novel methodology for active feature acquisition (AFA), the study of how to
sequentially acquire a dynamic (on a per instance basis) subset of features that minimizes …

For classification tasks, deep neural networks seek to minimize the average risk of
classification error during the training step. When some classes are more difficult to …

Methods based on neural networks have become more and more attractive in the medical
domain as Deep Learning frameworks mature and popularize. One application in this …