  Computer-aided diagnosis (CAD) based on histopathological imaging has
progressed rapidly in recent years with the rise of machine learning based
methodologies. Traditional approaches consist of training a classification
model using features extracted from the images, based on textures or
morphological properties. Recently, deep-learning based methods have been
applied directly to the raw (unprocessed) data. However, their usability is
impacted by the paucity of annotated data in the biomedical sector. In order to
leverage the learning capabilities of deep Convolutional Neural Nets (CNNs)
within the confines of limited labelled data, in this study we shall
investigate the transfer learning approaches that aim to apply the knowledge
gained from solving a source (e.g., non-medical) problem, to learn better
predictive models for the target (e.g., biomedical) task. As an alternative, we
shall further propose a new adaptive and compact CNN based architecture that
can be trained from scratch even on scarce and low-resolution data. Moreover,
we conduct quantitative comparative evaluations among the traditional methods,
transfer learning-based methods and the proposed adaptive approach for the
particular task of cancer detection and identification from scarce and
low-resolution histology images. Over the largest benchmark dataset formed for
this purpose, the proposed adaptive approach achieved a higher cancer detection
accuracy with a significant gap, whereas the deep CNNs with transfer learning
achieved a superior cancer identification.
