I saw, I conceived, I concluded: Progressive Concepts as Bottlenecks
- URL: http://arxiv.org/abs/2211.10630v1
- Date: Sat, 19 Nov 2022 09:31:19 GMT
- Title: I saw, I conceived, I concluded: Progressive Concepts as Bottlenecks
- Authors: Manxi Lin, Aasa Feragen, Zahra Bashir, Martin Gr{\o}nneb{\ae}k
Tolsgaard, Anders Nymark Christensen
- Abstract summary: Concept bottleneck models (CBMs) provide explainability and intervention during inference by correcting predicted, intermediate concepts.
This makes CBMs attractive for high-stakes decision-making.
We take the quality assessment of fetal ultrasound scans as a real-life use case for CBM decision support in healthcare.
- Score: 2.9398911304923447
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Concept bottleneck models (CBMs) include a bottleneck of human-interpretable
concepts providing explainability and intervention during inference by
correcting the predicted, intermediate concepts. This makes CBMs attractive for
high-stakes decision-making. In this paper, we take the quality assessment of
fetal ultrasound scans as a real-life use case for CBM decision support in
healthcare. For this case, simple binary concepts are not sufficiently
reliable, as they are mapped directly from images of highly variable quality,
for which variable model calibration might lead to unstable binarized concepts.
Moreover, scalar concepts do not provide the intuitive spatial feedback
requested by users.
To address this, we design a hierarchical CBM imitating the sequential expert
decision-making process of "seeing", "conceiving" and "concluding". Our model
first passes through a layer of visual, segmentation-based concepts, and next a
second layer of property concepts directly associated with the decision-making
task. We note that experts can intervene on both the visual and property
concepts during inference. Additionally, we increase the bottleneck capacity by
considering task-relevant concept interaction.
Our application of ultrasound scan quality assessment is challenging, as it
relies on balancing the (often poor) image quality against an assessment of the
visibility and geometric properties of standardized image content. Our
validation shows that -- in contrast with previous CBM models -- our CBM models
actually outperform equivalent concept-free models in terms of predictive
performance. Moreover, we illustrate how interventions can further improve our
performance over the state-of-the-art.
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