RadEdit: stress-testing biomedical vision models via diffusion image
  editing
Biomedical imaging datasets are often small and biased, meaning that
real-world performance of predictive models can be substantially lower than
expected from internal testing. This work proposes using generative image
editing to simulate dataset shifts and diagnose failure modes of biomedical
vision models; this can be used in advance of deployment to assess readiness,
potentially reducing cost and patient harm. Existing editing methods can
produce undesirable changes, with spurious correlations learned due to the
co-occurrence of disease and treatment interventions, limiting practical
applicability. To address this, we train a text-to-image diffusion model on
multiple chest X-ray datasets and introduce a new editing method RadEdit that
uses multiple masks, if present, to constrain changes and ensure consistency in
the edited images. We consider three types of dataset shifts: acquisition
shift, manifestation shift, and population shift, and demonstrate that our
approach can diagnose failures and quantify model robustness without additional
data collection, complementing more qualitative tools for explainable AI.
6583c5892ae92808739a98b4
Fernando Pérez-García
admin_assigned
2023-12-21T09:32:51.738Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/1669369101851-6278fbec17c069c72a11f35d.jpeg
Fernando Pérez-García
user
fepegar
6583c5892ae92808739a98b5
Sam Bond-Taylor
claimed_verified
2024-01-16T14:16:32.757Z
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Sam Bond-Taylor
user
sambt
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Pedro P. Sanchez
admin_assigned
2023-12-21T09:33:57.158Z
633bf51720ceb9fcae5abda3
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Pedro Sanchez
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PedroSanchez
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Boris van Breugel
admin_assigned
2023-12-21T09:34:05.112Z
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Boris van Breugel
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vanbreugel
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Daniel C. Castro
6583c5892ae92808739a98b9
Harshita Sharma
6583c5892ae92808739a98ba
Valentina Salvatelli
admin_assigned
2023-12-21T09:35:12.058Z
640dfb0bc364a086c633456e
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Valentina Salvatelli
user
vsalvatelli
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Maria T. A. Wetscherek
6583c5892ae92808739a98bc
Hannah Richardson
6583c5892ae92808739a98bd
Matthew P. Lungren
6583c5892ae92808739a98be
Aditya Nori
6583c5892ae92808739a98bf
Javier Alvarez-Valle
admin_assigned
2023-12-21T09:35:49.454Z
6278d875bba7c62cbaaf7693
/avatars/8d93e7c33acdfac853317d8fb5b0d485.svg
Javier Alvarez Valle
user
jaalvare
6583c5892ae92808739a98c0
Ozan Oktay
admin_assigned
2023-12-21T09:35:56.318Z
6274dd3eb43ccfeef481ce29
/avatars/9f5d2a25a74af3cb0a227f946041206d.svg
Ozan Oktay
user
ozanoktay
6583c5892ae92808739a98c1
Maximilian Ilse
RadEdit: stress-testing biomedical vision models via diffusion image
  editing
Biomedical imaging datasets are often small and biased, meaning that
real-world performance of predictive models can be substantially lower than
expected from internal testing. This work proposes using generative image
editing to simulate dataset shifts and diagnose failure modes of biomedical
vision models; this can be used in advance of deployment to assess readiness,
potentially reducing cost and patient harm. Existing editing methods can
produce undesirable changes, with spurious correlations learned due to the
co-occurrence of disease and treatment interventions, limiting practical
applicability. To address this, we train a text-to-image diffusion model on
multiple chest X-ray datasets and introduce a new editing method RadEdit that
uses multiple masks, if present, to constrain changes and ensure consistency in
the edited images. We consider three types of dataset shifts: acquisition
shift, manifestation shift, and population shift, and demonstrate that our
approach can diagnose failures and quantify model robustness without additional
data collection, complementing more qualitative tools for explainable AI.
Text-to-image diffusion model RadEdit is proposed for simulating dataset shifts in biomedical imaging to diagnose and quantify model failures without additional data collection.
generative image editing diffusion model dataset shifts text-to-image RadEdit masks acquisition shift manifestation shift population shift explainable AI