Needles in Haystacks: On Classifying Tiny Objects in Large Images

Understand

In some important computer vision domains, such as medical or hyperspectral imaging, we care about the classification of tiny objects in large images.

• However, most Convolutional Neural Networks (CNNs) for image classification were developed using biased datasets that contain large objects, in mostly central image positions.
• To assess whether classical CNN architectures work well for tiny object classification we build a comprehensive testbed containing two datasets: one derived from MNIST digits and one from histopathology images.
• This testbed allows controlled experiments to stress-test CNN architectures with a broad spectrum of signal-to-noise ratios.