2026-09-18T20:49:57Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1432072026-01-16T14:50:26Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Golland, Polina
Wang, Peiqi(Electrical engineer and computer scientist)
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2022-06-15T13:03:30Z
2022-06-15T13:03:30Z
2022-02
2022-03-04T20:59:47.960Z
https://hdl.handle.net/1721.1/143207
Adoption of machine learning models in healthcare requires end users’ trust in the system. Models that provide additional supportive evidence for their predictions promise to facilitate adoption. We define consistent evidence to be both compatible and sufficient with respect to model predictions. We propose measures of model inconsistency and regularizers that promote more consistent evidence. We demonstrate our ideas in the context of edema severity grading from chest radiographs. We demonstrate empirically that consistent models provide competitive performance while supporting interpretation.
S.M.
Massachusetts Institute of Technology
In Copyright - Educational Use Permitted
Copyright MIT
http://rightsstatements.org/page/InC-EDU/1.0/
Image Classification with Consistent Supporting Evidence
Thesis
application/pdf
Master
Master of Science in Electrical Engineering and Computer Science
Publication
unknown
unknown
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<Abstract>Adoption of machine learning models in healthcare requires end users’ trust in the system. Models that provide additional supportive evidence for their predictions promise to facilitate adoption. We define consistent evidence to be both compatible and sufficient with respect to model predictions. We propose measures of model inconsistency and regularizers that promote more consistent evidence. We demonstrate our ideas in the context of edema severity grading from chest radiographs. We demonstrate empirically that consistent models provide competitive performance while supporting interpretation.</Abstract>
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