Pathologists and radiologists spend years acquiring and refining their visual skills, and nobody can fully say which image features carry the diagnosis. Animal models have cracked open other human abilities that resist introspection. The pigeon visual system shares a great deal with ours, which makes it a candidate.
A pigeon, after training with food. Birds learned to distinguish benign from malignant human breast , and then generalized to image sets they had never seen before. They also detected cancer-relevant on mammogram images, so the skill was not limited to one kind of picture, and the training was nothing but food for a correct answer.
Pathologists and radiologists spend years acquiring and refining their medically essential visual skills, so there is considerable interest in how that process unfolds and which image features matter for accurate diagnosis. Key insights into human behavioral tasks can often come from an appropriate animal model.
Pigeons share many visual system properties with humans, and the authors report that they can serve as surrogate observers of medical images, a capability not previously documented. After training with , the birds distinguished benign from malignant human breast histopathology.
More importantly, they generalized that learning to novel image sets. Their histological accuracy, like that of humans, was modestly affected by the presence or absence of color as well as by degrees of image compression, and further training reduced both effects.
Radiology split the result in two. The birds detected cancer-relevant microcalcifications on mammograms, but on classification of suspicious mammographic densities, a task humans also find difficult, they proved capable only of image memorization and failed to .
wat. The pigeons generalized on histology and memorized on masses.
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A bird that learns the task without a medical education shows how much of it lives in the image itself.
How do we know?
Levenson and colleagues trained pigeons with differential food reinforcement on real medical images, then tested them on novel examples and varied color and image compression.
What is still unknown
One task defeated them completely, and no bird is being asked to sign a report.
Learn something?
Where to next
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