Towards Out-of-Distribution Detection for breast cancer classification
  in Point-of-Care Ultrasound Imaging
Jennie Karlsson Marisa Wodrich Niels Christian Overgaard Freja Sahlin Kristina Lång Anders Heyden Ida Arvidsson
Deep learning has shown to have great potential in medical applications. In
critical domains as such, it is of high interest to have trustworthy algorithms
which are able to tell when reliable assessments cannot be guaranteed.
Detecting out-of-distribution (OOD) samples is a crucial step towards building
a safe classifier. Following a previous study, showing that it is possible to
classify breast cancer in point-of-care ultrasound images, this study
investigates OOD detection using three different methods: softmax, energy score
and deep ensembles. All methods are tested on three different OOD data sets.
The results show that the energy score method outperforms the softmax method,
performing well on two of the data sets. The ensemble method is the most
robust, performing the best at detecting OOD samples for all three OOD data
sets.