  Predictive modeling is useful but very challenging in biological image
analysis due to the high cost of obtaining and labeling training data. For
example, in the study of gene interaction and regulation in Drosophila
embryogenesis, the analysis is most biologically meaningful when in situ
hybridization (ISH) gene expression pattern images from the same developmental
stage are compared. However, labeling training data with precise stages is very
time-consuming even for evelopmental biologists. Thus, a critical challenge is
how to build accurate computational models for precise developmental stage
classification from limited training samples. In addition, identification and
visualization of developmental landmarks are required to enable biologists to
interpret prediction results and calibrate models. To address these challenges,
we propose a deep two-step low-shot learning framework to accurately classify
ISH images using limited training images. Specifically, to enable accurate
model training on limited training samples, we formulate the task as a deep
low-shot learning problem and develop a novel two-step learning approach,
including data-level learning and feature-level learning. We use a deep
residual network as our base model and achieve improved performance in the
precise stage prediction task of ISH images. Furthermore, the deep model can be
interpreted by computing saliency maps, which consist of pixel-wise
contributions of an image to its prediction result. In our task, saliency maps
are used to assist the identification and visualization of developmental
landmarks. Our experimental results show that the proposed model can not only
make accurate predictions, but also yield biologically meaningful
interpretations. We anticipate our methods to be easily generalizable to other
biological image classification tasks with small training datasets.
