  We explore the potential of CNN-based models for gallbladder cancer (GBC)
detection from ultrasound (USG) images as no prior study is known. USG is the
most common diagnostic modality for GB diseases due to its low cost and
accessibility. However, USG images are challenging to analyze due to low image
quality, noise, and varying viewpoints due to the handheld nature of the
sensor. Our exhaustive study of state-of-the-art (SOTA) image classification
techniques for the problem reveals that they often fail to learn the salient GB
region due to the presence of shadows in the USG images. SOTA object detection
techniques also achieve low accuracy because of spurious textures due to noise
or adjacent organs. We propose GBCNet to tackle the challenges in our problem.
GBCNet first extracts the regions of interest (ROIs) by detecting the GB (and
not the cancer), and then uses a new multi-scale, second-order pooling
architecture specializing in classifying GBC. To effectively handle spurious
textures, we propose a curriculum inspired by human visual acuity, which
reduces the texture biases in GBCNet. Experimental results demonstrate that
GBCNet significantly outperforms SOTA CNN models, as well as the expert
radiologists. Our technical innovations are generic to other USG image analysis
tasks as well. Hence, as a validation, we also show the efficacy of GBCNet in
detecting breast cancer from USG images. Project page with source code, trained
models, and data is available at https://gbc-iitd.github.io/gbcnet
