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Improving the Ability of Deep Neural Networks to Use Information from Multiple Views in Breast Cancer Screening Nan Wu , Stanis aw Jastrz bski, Jungkyu Park, Linda Moy, Kyunghyun Cho, Krzysztof J. Geras Breast cancer screening with multiple

  1. Improving the Ability of Deep Neural Networks to Use Information from Multiple Views in Breast Cancer Screening Nan Wu , Stanis ł aw Jastrz ę bski, Jungkyu Park, Linda Moy, Kyunghyun Cho, Krzysztof J. Geras

  2. Breast cancer screening with multiple views screening mammography

  3. Breast cancer screening with multiple views screening mammography

  4. Breast cancer screening with multiple views screening mammography Cranio Caudal (CC) Medio Lateral Oblique (MLO)

  5. Research question

  6. Research question Radiologists Using both views is essential to make an accurate diagnosis in breast cancer screening.

  7. Research question Radiologists Using both views is essential to make an accurate diagnosis in breast cancer screening. Multiview deep neural networks Classifier Fusion operation Does it utilize information in DNN DNN both views? CC MLO

  8. Evidence of difficulties in multiview learning

  9. Evidence of difficulties in multiview learning [1] Nan Wu et al. Deep neural networks improve radiologists’ performance in breast cancer screening. IEEE Transactions on Medical Imaging, 2019.

  10. Evidence of difficulties in multiview learning Joint network Image-wise network [1] Nan Wu et al. Deep neural networks improve radiologists’ performance in breast cancer screening. IEEE Transactions on Medical Imaging, 2019.

  11. Evidence of difficulties in multiview learning Joint network Image-wise network [1] Nan Wu et al. Deep neural networks improve radiologists’ performance in breast cancer screening. IEEE Transactions on Medical Imaging, 2019.

  12. Evidence of difficulties in multiview learning Joint network Image-wise network [1] Nan Wu et al. Deep neural networks improve radiologists’ performance in breast cancer screening. IEEE Transactions on Medical Imaging, 2019.

  13. Evidence of difficulties in multiview learning Joint network Image-wise network AUC joint Image-wise Image-only 0.822 0.830 0.875 Image-and-heatmaps 0.860 [1] Nan Wu et al. Deep neural networks improve radiologists’ performance in breast cancer screening. IEEE Transactions on Medical Imaging, 2019.

  14. Evidence of difficulties in multiview learning Joint network Image-wise network AUC joint Image-wise Image-only 0.822 0.830 0.875 Image-and-heatmaps 0.860 [1] Nan Wu et al. Deep neural networks improve radiologists’ performance in breast cancer screening. IEEE Transactions on Medical Imaging, 2019. [2] Weiyao Wang, Du Tran, and Matt Feiszli. What makes training multi-modal networks hard? arXiv:1905.12681, 2019. [3] Mohammad Hashir, Hadrien Bertrand, Joseph Paul Cohen. Quantifying the Value of Lateral Views in Deep Learning for Chest X-rays. MIDL 2020.

  15. What makes using both views of the breast difficult?

  16. What makes using both views of the breast difficult? Classifier Classifier DNN DNN CC MLO

  17. What makes using both views of the breast difficult? Classifier Fusion operation Classifier Classifier DNN DNN DNN DNN CC MLO CC MLO

  18. How to improve its ability in utilizing information in both views of the breast?

  19. How to improve its ability in utilizing information in both views of the breast? Two methods that can help: Modality Dropout and Sharing weights between part operating on each view. +

  20. Improving the Ability of Deep Neural Networks to Use Information from Multiple Views in Breast Cancer Screening Thank you!

  21. Improving the Ability of Deep Neural Networks to Use Information from Multiple Views in Breast Cancer Screening Nan Wu , Stanis ł aw Jastrz ę bski, Jungkyu Park, Linda Moy, Kyunghyun Cho, Krzysztof J. Geras Classifier Fusion operation screening mammography DNN DNN CC MLO • What makes using both views of the breast di ffi cult? Cranio Caudal Medio Lateral Oblique • How to improve its ability in utilizing information in both views of the breast?

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