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Uploaded byMiguel González-Fierro
Deep Learning for Lung Cancer Detection
The document discusses a competition related to lung cancer detection using AI, where $1 million in prizes is at stake. Dr. Miguel Fierro is involved in AI immersion techniques, applying various machine learning models to analyze CT scan images from 1,595 patients. It highlights methods such as weight transference, using ImageNet datasets, and performance comparisons between traditional programming and AI approaches.
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Deep Learning for Lung Cancer Detection
- 4. $1 million inprizes ! CT scan: horizontal section of lung Determine whether a patient has cancer or notcompetition AI Immersion - Dr. Miguel Fierro - @miguelgfierro
- 5. 1595 patients witha diagnostic 200-500 scans per patient Images of 512x512 AI Immersion - Dr. Miguel Fierro - @miguelgfierro
- 6. ImageNet dataset Lungcancer dataset weight transference AI Immersion - Dr. Miguel Fierro - @miguelgfierro
- 7. 3 224 224 ResNet N-1 layers penultimate layer CNTK kbatch of images = 1 patient features AI Immersion - Dr. Miguel Fierro - @miguelgfierro
- 8. 3 224 224 ResNet N-1 layers penultimate layer no cancer CNTKLightGBM Boosted tree k batch of images = 1 patient features AI Immersion - Dr. Miguel Fierro - @miguelgfierro
- 9. Demo AI Immersion -Dr. Miguel Fierro - @miguelgfierro
- 10. AI Immersion -Dr. Miguel Fierro - @miguelgfierro https://github.com/juliandewit/kaggle_ndsb2017 2th Place in the Lung Cancer Competition
- 11. Traditional python vsSQL python AI Immersion - Dr. Miguel Fierro - @miguelgfierro
- 12. Traditional python vsSQL python AI Immersion - Dr. Miguel Fierro - @miguelgfierro
- 13. N’ from python_package.lung_cancer_lib importscore_patient_with_deep_learning_model PredictionResult = score_patient_with_deep_learning_model(PatientIndex) ‘ SQL python example AI Immersion - Dr. Miguel Fierro - @miguelgfierro
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- #5 This slide is required. Do NOT delete. This should be the first slide after your Title Slide. This slide should describe what your goals are for this session. This information lets your audience know what you are trying to accomplish with your talk or tutorial—ie, what value will attendees get by investing 25 minutes or 2 hours of their time listening to you. You should not spend more than 1 minute presenting this slide. General examples of session goals could be (you will have to create your own specific goals): Introduce a new technique or approach to solve a customer problem Compare two approaches and explain why one is superior Describe a project and the learnings that audience members can apply from it Teach audience members how to use a specific technology