  In recent years, automated Gallbladder Cancer (GBC) detection has gained the
attention of researchers. Current state-of-the-art (SOTA) methodologies relying
on ultrasound sonography (US) images exhibit limited generalization,
emphasizing the need for transformative approaches. We observe that individual
US frames may lack sufficient information to capture disease manifestation.
This study advocates for a paradigm shift towards video-based GBC detection,
leveraging the inherent advantages of spatiotemporal representations. Employing
the Masked Autoencoder (MAE) for representation learning, we address
shortcomings in conventional image-based methods. We propose a novel design
called FocusMAE to systematically bias the selection of masking tokens from
high-information regions, fostering a more refined representation of
malignancy. Additionally, we contribute the most extensive US video dataset for
GBC detection. We also note that, this is the first study on US video-based GBC
detection. We validate the proposed methods on the curated dataset, and report
a new state-of-the-art (SOTA) accuracy of 96.4% for the GBC detection problem,
against an accuracy of 84% by current Image-based SOTA - GBCNet, and RadFormer,
and 94.7% by Video-based SOTA - AdaMAE. We further demonstrate the generality
of the proposed FocusMAE on a public CT-based Covid detection dataset,
reporting an improvement in accuracy by 3.3% over current baselines. The source
code and pretrained models are available at:
https://gbc-iitd.github.io/focusmae
