  We present an algorithm for multi-scale tumor (chimeric cell) detection in
high resolution slide scans. The broad range of tumor sizes in our dataset pose
a challenge for current Convolutional Neural Networks (CNN) which often fail
when image features are very small (8 pixels). Our approach modifies the
effective receptive field at different layers in a CNN so that objects with a
broad range of varying scales can be detected in a single forward pass. We
define rules for computing adaptive prior anchor boxes which we show are
solvable under the equal proportion interval principle. Two mechanisms in our
CNN architecture alleviate the effects of non-discriminative features prevalent
in our data - a foveal detection algorithm that incorporates a cascade
residual-inception module and a deconvolution module with additional context
information. When integrated into a Single Shot MultiBox Detector (SSD), these
additions permit more accurate detection of small-scale objects. The results
permit efficient real-time analysis of medical images in pathology and related
biomedical research fields.
