  Creating large-scale and well-annotated datasets to train AI algorithms is
crucial for automated tumor detection and localization. However, with limited
resources, it is challenging to determine the best type of annotations when
annotating massive amounts of unlabeled data. To address this issue, we focus
on polyps in colonoscopy videos and pancreatic tumors in abdominal CT scans;
both applications require significant effort and time for pixel-wise annotation
due to the high dimensional nature of the data, involving either temporary or
spatial dimensions. In this paper, we develop a new annotation strategy, termed
Drag&Drop, which simplifies the annotation process to drag and drop. This
annotation strategy is more efficient, particularly for temporal and volumetric
imaging, than other types of weak annotations, such as per-pixel, bounding
boxes, scribbles, ellipses, and points. Furthermore, to exploit our Drag&Drop
annotations, we develop a novel weakly supervised learning method based on the
watershed algorithm. Experimental results show that our method achieves better
detection and localization performance than alternative weak annotations and,
more importantly, achieves similar performance to that trained on detailed
per-pixel annotations. Interestingly, we find that, with limited resources,
allocating weak annotations from a diverse patient population can foster models
more robust to unseen images than allocating per-pixel annotations for a small
set of images. In summary, this research proposes an efficient annotation
strategy for tumor detection and localization that is less accurate than
per-pixel annotations but useful for creating large-scale datasets for
screening tumors in various medical modalities.
