  In biomedical imaging, deep learning-based methods are state-of-the-art for
every modality (virtual slides, MRI, etc.) In histopathology, these methods can
be used to detect certain biomarkers or classify lesions. However, such
techniques require large amounts of data to train high-performing models which
can be intrinsically difficult to acquire, especially when it comes to scarce
biomarkers. To address this challenge, we use a single, pre-trained, deep
embeddings extractor to convert images into deep features and train small,
dedicated classification head on these embeddings for each classification task.
This approach offers several benefits such as the ability to reuse a single
pre-trained deep network for various tasks; reducing the amount of labeled data
needed as classification heads have fewer parameters; and accelerating training
time by up to 1000 times, which allows for much more tuning of the
classification head. In this work, we perform an extensive comparison of
various open-source backbones and assess their fit to the target histological
image domain. This is achieved using a novel method based on a proxy
classification task. We demonstrate that thanks to this selection method, an
optimal feature extractor can be selected for different tasks on the target
domain. We also introduce a feature space augmentation strategy which proves to
substantially improve the final metrics computed for the different tasks
considered. To demonstrate the benefit of such backbone selection and
feature-space augmentation, our experiments are carried out on three separate
classification tasks and show a clear improvement on each of them:
microcalcifications (29.1% F1-score increase), lymph nodes metastasis (12.5%
F1-score increase), mitosis (15.0% F1-score increase).
