DeepMiner: Discovering Interpretable Representations for Mammogram Classification and Explanation

Jimmy Wu, Bolei Zhou, Diondra Peck, Scott Hsieh, Vandana Dialani, Lester Mackey, Geneviève Patterson

Open source

DOI: 10.1162/99608f92.8b81b005
Published: 2021-10-28
Container: Harvard Data Science Review
Publisher: MIT Press
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1162/99608f92.8b81b005,
  title = {DeepMiner: Discovering Interpretable Representations for Mammogram Classification and Explanation},
  author = {Jimmy Wu and Bolei Zhou and Diondra Peck and Scott Hsieh and Vandana Dialani and Lester Mackey and Geneviève Patterson},
  year = {2021},
  journal = {Harvard Data Science Review},
  doi = {10.1162/99608f92.8b81b005},
  url = {https://doi.org/10.1162/99608f92.8b81b005}
}

RIS

TY  - JOUR
TI  - DeepMiner: Discovering Interpretable Representations for Mammogram Classification and Explanation
AU  - Jimmy Wu
AU  - Bolei Zhou
AU  - Diondra Peck
AU  - Scott Hsieh
AU  - Vandana Dialani
AU  - Lester Mackey
AU  - Geneviève Patterson
PY  - 2021
JO  - Harvard Data Science Review
DO  - 10.1162/99608f92.8b81b005
UR  - https://doi.org/10.1162/99608f92.8b81b005
ER  -

APA

Wu, J., Zhou, B., Peck, D., Hsieh, S., Dialani, V., Mackey, L., & Patterson, G. (2021). DeepMiner: Discovering Interpretable Representations for Mammogram Classification and Explanation. Harvard Data Science Review. https://doi.org/10.1162/99608f92.8b81b005

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crossref · retrieved 2026-11-10T19:53:53.569Z