Cancer metastasis detection with neural conditional random field
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The document discusses Baidu Research's use of deep learning to help pathologists find tumors. It describes how reviewing pathology slides can be difficult due to their large size and complexity. Most existing methods use patch-based deep CNN classifiers to label patches as tumor or normal, but these do not capture spatial correlations. The document presents Baidu's new NCRF method, which captures spatial correlations and outperforms previous methods, achieving an FROC score of 0.8096 on a test set. The paper on this method was accepted to MIDL 2018 and the code has been open sourced.
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Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
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https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
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Epistemic Interaction - tuning interfaces to provide information for AI support
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
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Cancer metastasis detection with neural conditional random field
- 1. || Baidu Research Silicon Valley AI Lab Using Deep Learning to Help Pathologists Find Tumors Yi Li, Baidu Silicon Valley AI Lab 11/14/2018Yi Li 1 MLconf 2018, San Francisco
- 2. || Baidu Research Silicon Valley AI Lab 11/14/2018Yi Li 2 Pathology for cancer diagnosis (source from Google Image)
- 3. || Baidu Research Silicon Valley AI Lab 11/14/2018Yi Li 3 Reviewing pathology slides can be difficult… 40X WSI 1GB+, ~100K * ~100K pixels Tumor Deep learning?
- 4. || Baidu Research Silicon Valley AI Lab 11/14/2018Yi Li 4 Most existing methods Patch based deep CNN classifier Tumor or Normal? (source from Lunit Inc) D. Wang, et al. 2016 B. Geçer, 2016 L. Hou, 2016 Y. Liu, 2017 H. Lin, 2017 …
- 5. || Baidu Research Silicon Valley AI Lab 11/14/2018Yi Li 5 Hard negatives Tumor?Tumor?
- 6. || Baidu Research Silicon Valley AI Lab 11/14/2018Yi Li 6 Google
- 7. || Baidu Research Silicon Valley AI Lab 11/14/2018Yi Li 7 Our method(NCRF)
- 8. || Baidu Research Silicon Valley AI Lab 11/14/2018Yi Li 8 Our method captures spatial correlations
- 9. || Baidu Research Silicon Valley AI Lab 11/14/2018Yi Li 9 Our method vs previous Raw image Pathologist annotation Previous method Our method
- 10. || Baidu Research Silicon Valley AI Lab 11/14/2018Yi Li 10 Our method vs previous championship ours = 0.8096
- 11. || Baidu Research Silicon Valley AI Lab 11/14/2018Yi Li 11 Our method vs previous championship FROC curves on the test set with random seed 0
- 12. || Baidu Research Silicon Valley AI Lab 11/14/2018Yi Li 12 Paper accepted to MIDL 2018
- 13. || Baidu Research Silicon Valley AI Lab 11/14/2018Yi Li 13 Code is open sourced!!! https://github.com/baidu-research/NCRF
- 14. || Baidu Research Silicon Valley AI Lab 11/14/2018Yi Li 14 Press report
- 15. || Baidu Research Silicon Valley AI Lab 11/14/2018Yi Li 15 Thank you! liyi17@baidu.com Questions?
Editor's Notes
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