Scientists meet Entrepreneurs - AI & Machine Learning, Dima Fishman, University of Tartu
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Scientists meet Entrepreneurs - AI & Machine Learning, Dima Fishman, University of Tartu
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Scientists meet Entrepreneurs - AI & Machine Learning, Dima Fishman, University of Tartu
- 1. Deep Learning in Medicine and Computational Biology Dmytro Fishman (dmytro@ut.ee)
- 3. Cat
- 4. Cat
- 8. Cat Representation Cat Not a cat Machine Learning
- 10. 1.2 million images 1000 categories
- 13. Errors 2010 2011 2012 2013 2014 2015 2016 28% 26% 16% 12% 7% 3% <3% AlexNet (A. Krizhevsky et al. 2012) http://karpathy.github.io/2014/09/02/what-i-learned-from-competing-against-a-convnet-on-imagenet/
- 14. Errors 2010 2011 2012 2013 2014 2015 2016 28% 26% 16% 12% 7% 3% <3% Hypothetical super- dedicated fine- grained expert ensemble of human labelers AlexNet (A. Krizhevsky et al. 2012) http://karpathy.github.io/2014/09/02/what-i-learned-from-competing-against-a-convnet-on-imagenet/
- 15. Errors 2010 2011 2012 2013 2014 2015 2016 28% 26% 16% 12% 7% 3% <3% Hypothetical super- dedicated fine- grained expert ensemble of human labelers AlexNet (A. Krizhevsky et al. 2012) https://www.semanticscholar.org/paper/Fine-grained-Categorization-Short-Summary-of-our-E-G%C3%B6ring-Freytag/0f3b7d252c236d47cf4185fd81bbb40767baf3d8 Different breeds The same breed
- 23. Medicine is really complex
- 25. ReconMap 2.0
- 26. ReconMap 2.0
- 28. Deep Learning Super rapid growth
- 29. Deep Learning Fever Super rapid growth
- 31. Bloodletting
- 34. Nevertheless…
- 35. https://pancreaticcanceraction.org/wp-content/uploads/2012/10/Trends-in-cancer-survival-by-tumour-site-1971_2011.png Year1970 2010 Trends in cancer survival Skin Breast Prostate Leukaemia Bowel Kidney Myeloma Ovarian Pancreatic
- 36. Merck Molecular Activity Challenge 2012
- 38. Diabetic Retinopathy Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs Dermatologist-level classification of skin cancer with deep neural networks Skin Cancer
- 39. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs Dermatologist-level classification of skin cancer with deep neural networks Skin Cancer Diabetic Retinopathy
- 45. Diagnostics done manually 9 - 12 minutes per patient
- 46. 128 175 images for training
- 47. 128 175 images for training 54 US licensed ophthalmologists
- 48. 128 175 images for training 54 US licensed ophthalmologists Classify into healthy, mild and severe
- 49. Sample annotation 1st doctor 2nd doctor 3rd doctor 4th doctor Healthy Disease
- 50. 0 0 0 0 1st doctor 2nd doctor 3rd doctor 4th doctor Healthy Disease Sample annotation
- 51. Healthy Disease 0 1st doctor 2nd doctor 3rd doctor 4th doctor Sample annotation
- 52. Healthy Disease 0 1 4 3 4 1st doctor 2nd doctor 3rd doctor 4th doctor Sample annotation
- 53. Healthy Disease 0 Major vote is used 1 4 3 4 1st doctor 2nd doctor 3rd doctor 4th doctor Sample annotation
- 54. Healthy Disease 0 4 Algorithm might only marginally outperform doctors 1st doctor 2nd doctor 3rd doctor 4th doctor Sample annotation
- 56. Algorithm vs Ophthalmologists Sensitivity,% 100 - Specificity, % 0100 0 100 AUC of 97.4%
- 57. Algorithm vs Ophthalmologists Sensitivity,% 100 - Specificity, % 0100 0 100 AUC of 97.4% The black curve is ROC for the Deep Learning algorithm
- 58. Algorithm vs Ophthalmologists Sensitivity,% 100 - Specificity, % 0100 0 100 AUC of 97.4% Points on ROC are performances of individual ophthalmologists The black curve is ROC for the Deep Learning algorithm
- 59. Performances are very similar Algorithm vs Ophthalmologists Points on ROC are performances of individual ophthalmologists The black curve is ROC for the Deep Learning algorithm Sensitivity,% 100 - Specificity, % 0100 0 100 AUC of 97.4%
- 60. Deep Learning algorithm can operate in any point on the curve Algorithm vs Ophthalmologists Sensitivity,% 100 - Specificity, % 0100 0 100 AUC of 97.4%
- 61. Deep Learning algorithm can operate in any point on the curve Sensitivity,% 100 - Specificity, % 0100 0 100 AUC of 97.4% Algorithm vs Ophthalmologists High specificity mode (diagnosis)
- 62. Deep Learning algorithm can operate in any point on the curve Sensitivity,% 100 - Specificity, % 0100 0 100 AUC of 97.4% Algorithm vs Ophthalmologists High specificity mode (diagnosis) High sensitivity mode (screening)
- 63. Deep Learning algorithm can operate in any point on the curve Sensitivity,% 100 - Specificity, % 0100 0 100 AUC of 97.4% Algorithm vs Ophthalmologists High specificity mode (diagnosis) High sensitivity mode (screening) While ophthalmologists’s mode is fixed by experience
- 64. Diabetic Retinopathy Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs Dermatologist-level classification of skin cancer with deep neural networks Skin Cancer
- 65. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs Dermatologist-level classification of skin cancer with deep neural networks Diabetic Retinopathy Skin Cancer
- 67. ?!
- 72. 0 1
- 73. 0 1
- 74. 0 1 90% VS 14% Difference in survival rates is drastic
- 75. 0 1 90% VS 14%
- 77. 129 450 images for training set
- 78. 129 450 images for training set 21 board- certified dermatologists
- 80. Skin disease
- 89. 9% 1% 5% 4.5% 8.5% 2% 2% 4% 1.5% 0.5% 1.5% 4.5% 1% 3% 7% 15% 10% 1%3.3% 6.3% 0.4% 4% 5%
- 90. 9% 1% 5% 4.5% 8.5% 2% 2% 4% 1.5% 0.5% 1.5% 4.5% 1% 3% 7% 15% 10% 1%3.3% 6.3% 0.4% 4% 5% 15% Sum leaf nodes
- 91. 9% 1% 5% 4.5% 8.5% 2% 2% 4% 1.5% 0.5% 1.5% 4.5% 1% 3% 7% 15% 10% 1%3.3% 6.3% 0.4% 4% 5% 15% 13% 8% 10% 5.5% 2% 20% 25% 15%
- 95. Algorithm vs Dermatologists Specificity,% Sensitivity, % AUC of 96% Carcinoma: 135 images Dermatologists (25)
- 96. Each of the cases was verified by biopsy Specificity,% Sensitivity, % AUC of 96% Carcinoma: 135 images Dermatologists (25) Algorithm vs Dermatologists
- 97. Specificity,% Sensitivity, % AUC of 96% Carcinoma: 135 images Dermatologists (25) Algorithm vs Dermatologists
- 98. Specificity,% Sensitivity, % AUC of 96% Carcinoma: 135 images Dermatologists (25) Algorithm vs Dermatologists Performance of the algorithm was compared to dermatologists
- 99. Performance of the algorithm was compared to dermatologists Average dermatologist’s performance was marked as Specificity,% Sensitivity, % AUC of 96% Carcinoma: 135 images Dermatologists (25) Algorithm vs Dermatologists
- 100. Performance of the algorithm was compared to dermatologists Average dermatologist’s performance was marked as Specificity,% Sensitivity, % AUC of 96% Carcinoma: 135 images Dermatologists (25) Algorithm vs Dermatologists
- 101. Specificity,% Sensitivity, % AUC of 96% Specificity,% Sensitivity, % AUC of 94% Specificity,% Sensitivity, % AUC of 91% Carcinoma: 135 images Melanoma: 130 images Melanoma: 111 images Dermatologists (25) Dermatologists (22) Dermatologists (21) Algorithm vs Dermatologists
- 102. Specificity,% Sensitivity, % AUC of 96% Specificity,% Sensitivity, % AUC of 94% Specificity,% Sensitivity, % AUC of 91% Carcinoma: 135 images Melanoma: 130 images Melanoma: 111 images Dermatologists (25) Dermatologists (22) Dermatologists (21) Algorithm vs Dermatologists Across all biopsy verified datasets Deep Neural Network was superior
- 103. Diabetic Retinopathy Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs Dermatologist-level classification of skin cancer with deep neural networks Skin Cancer https://jamanetwork.com/journals/jama/fullarticle/2588763 https://www.nature.com/nature/journal/v542/n7639/full/ nature21056.html
- 104. Few more interesting applications
- 105. Diagnosing Parkinson from voice (Al-Fatlawi et al., 2016) Detection of hypoglycemic episodes in children (San et al., 2016) HemoglobinA1c 03.2010 Timeline 07.2010 12.2010 02.2011 04.2011 ? Painintensity Frames Pain estimation from video (Zhou et al., 2016) Predicting subsequent hospitalisation (Choi et al., 2016)
- 108. Sleep
- 109. Sleep Hunger
- 110. Sleep Pain Hunger
- 111. Seems like revolution did not happened
- 112. Why Deep Learning has not revolutionised medicine yet?
- 113. Chart of possible reasons why deep learning may fail to revolutionise medicineLikelihood Effect UnlikelyHighlylikely Not nice, but ok Terrible consequences
- 114. We may fail to compose large enough datasets
- 115. Collecting data in medicine is very expensive We may fail to compose large enough datasets
- 116. Collecting data in medicine is very expensive Medical data is often protected (for a good reason) We may fail to compose large enough datasets
- 117. We may fail to compose large enough datasets
- 118. We may fail to compose large enough datasets
- 119. We may fail to compose large enough datasets
- 120. We can build a model that can distinguish them from other objects We may fail to compose large enough datasets
- 121. We can build a model that can distinguish them from other objects We may fail to compose large enough datasets
- 122. We can build a model that can distinguish them from other objects We cannot build a robust representation for all of them We may fail to compose large enough datasets
- 123. We can build a model that can distinguish them from other objects We would need a separate ImageNet for each type We may fail to compose large enough datasets We cannot build a robust representation for all of them
- 126. Chart of possible reasons why deep learning may fail to revolutionise medicineLikelihood Effect UnlikelyHighlylikely Not nice, but ok Terrible consequences Data
- 128. There is a ABCD rule they learned in college How doctors diagnose melanomas?
- 129. There is a ABCD rule they learned in college Melanomas are Asymmetrical How doctors diagnose melanomas?
- 130. There is a ABCD rule they learned in college Melanomas are Asymmetrical How doctors diagnose melanomas?
- 131. There is a ABCD rule they learned in college Melanomas are Asymmetrical How doctors diagnose melanomas? Their Borders are uneven
- 132. There is a ABCD rule they learned in college Melanomas are Asymmetrical Their Borders are uneven How doctors diagnose melanomas?
- 133. There is a ABCD rule they learned in college Melanomas are Asymmetrical Colour can be patchy and variegated How doctors diagnose melanomas? Their Borders are uneven
- 134. There is a ABCD rule they learned in college Melanomas are Asymmetrical Colour can be patchy and variegated Their Borders are uneven How doctors diagnose melanomas?
- 135. There is a ABCD rule they learned in college Melanomas are Asymmetrical Colour can be patchy and variegated How doctors diagnose melanomas? Their Borders are uneven
- 136. There is a ABCD rule they learned in college Colour can be patchy and variegated their Diameter is usually > 6 millimetres How doctors diagnose melanomas? Melanomas are Asymmetrical Their Borders are uneven
- 137. There is a ABCD rule they learned in college Melanomas are Asymmetrical Their Borders are uneven Colour can be patchy and variegated their Diameter is usually > 6 millimetres How doctors diagnose melanomas?
- 138. How computers diagnose melanomas?
- 139. How computers diagnose melanomas?
- 140. How computers diagnose melanomas? Melanoma
- 141. How computers diagnose melanomas? Melanoma
- 143. Chart of possible reasons why deep learning may fail to revolutionise medicineLikelihood Effect UnlikelyHighlylikely Not nice, but ok Terrible consequences DataInterpretability
- 144. Your computer
- 145. Your computer ACCCTTAAGGAGATCCTT TAACCGAACCTCACCCTT AAGGAGATCCTTTAACCG CCCTTTTATTCCTATTACGT Read 3.5 B more…
- 146. Gene Technology Your computer ACCCTTAAGGAGATCCTT TAACCGAACCTCACCCTT AAGGAGATCCTTTAACCG CCCTTTTATTCCTATTACGT Read 3.5 B more… Genome
- 147. Genome Schizofrenia - 0.15% Diabetes - 0.05% Cancer - 0.01% Gene Technology Your computer ACCCTTAAGGAGATCCTT TAACCGAACCTCACCCTT AAGGAGATCCTTTAACCG CCCTTTTATTCCTATTACGT Read 3.5 B more…
- 148. Risks Genome Schizofrenia - 0.15% Diabetes - 0.05% Cancer - 0.01% Gene Technology Your computer ACCCTTAAGGAGATCCTT TAACCGAACCTCACCCTT AAGGAGATCCTTTAACCG CCCTTTTATTCCTATTACGT Read 3.5 B more…
- 149. Risks Genome Schizofrenia - 0.15% Diabetes - 0.05% Cancer - 0.01% Gene Technology Your computer ACCCTTAAGGAGATCCTT TAACCGAACCTCACCCTT AAGGAGATCCTTTAACCG CCCTTTTATTCCTATTACGT Read 3.5 B more…
- 150. Application Cool company Risks Genome Schizofrenia - 0.15% Diabetes - 0.05% Cancer - 0.01% Gene Technology Your computer ACCCTTAAGGAGATCCTT TAACCGAACCTCACCCTT AAGGAGATCCTTTAACCG CCCTTTTATTCCTATTACGT Read 3.5 B more…
- 151. Application Cool company Risks Genome Schizofrenia - 0.15% Diabetes - 0.05% Cancer - 0.01% Gene Technology Your computer ACCCTTAAGGAGATCCTT TAACCGAACCTCACCCTT AAGGAGATCCTTTAACCG CCCTTTTATTCCTATTACGT Read 3.5 B more…
- 152. Risks Application Cool company Risks Genome Schizofrenia - 0.15% Diabetes - 0.05% Cancer - 0.01% Gene Technology Your computer ACCCTTAAGGAGATCCTT TAACCGAACCTCACCCTT AAGGAGATCCTTTAACCG CCCTTTTATTCCTATTACGT Read 3.5 B more…
- 153. Your computer ACCCTTAAGGAGATCCTT TAACCGAACCTCACCCTT AAGGAGATCCTTTAACCG CCCTTTTATTCCTATTACGT Read 3.5 B more…
- 154. Encrypting your genome Your computer ACCCTTAAGGAGATCCTT TAACCGAACCTCACCCTT AAGGAGATCCTTTAACCG CCCTTTTATTCCTATTACGT Read 3.5 B more…
- 155. !#$#*(#$@&#$@^@ %#@%@^#@#$*(@#) $#@&#$@^#@$)@# $#@*#@$#@####@@!! Read 3.5 B more… Encrypting your genome Your computer
- 156. Encrypted genome Gene Technology !#$#*(#$@&#$@^@ %#@%@^#@#$*(@#) $#@&#$@^#@$)@# $#@*#@$#@####@@!! Read 3.5 B more… Your computer
- 157. #@#$!!@@# - #?@% #@$(%&&& - ?@#% $&@))#^%? - ??@% Encrypted genome Gene Technology !#$#*(#$@&#$@^@ %#@%@^#@#$*(@#) $#@&#$@^#@$)@# $#@*#@$#@####@@!! Read 3.5 B more… Your computer
- 158. Encrypted risks #@#$!!@@# - #?@% #@$(%&&& - ?@#% $&@))#^%? - ??@% Encrypted genome Gene Technology !#$#*(#$@&#$@^@ %#@%@^#@#$*(@#) $#@&#$@^#@$)@# $#@*#@$#@####@@!! Read 3.5 B more… Your computer
- 159. Encrypted risks #@#$!!@@# - #?@% #@$(%&&& - ?@#% $&@))#^%? - ??@% Encrypted genome Gene Technology !#$#*(#$@&#$@^@ %#@%@^#@#$*(@#) $#@&#$@^#@$)@# $#@*#@$#@####@@!! Read 3.5 B more… Your computer Decrypt your risks with your private key
- 160. Encrypted risks #@#$!!@@# - #?@% #@$(%&&& - ?@#% $&@))#^%? - ??@% Encrypted genome Gene Technology Your computer Decrypt your risks with your private key Schizofrenia - 0.15% Diabetes - 0.05% Cancer - 0.01%
- 161. and start panicking changing your lifestyle Encrypted risks #@#$!!@@# - #?@% #@$(%&&& - ?@#% $&@))#^%? - ??@% Encrypted genome Gene Technology Your computer Decrypt your risks with your private key Schizofrenia - 0.15% Diabetes - 0.05% Cancer - 0.01%
- 162. Chart of possible reasons why deep learning may fail to revolutionise medicineLikelihood Effect UnlikelyHighlylikely Not nice, but ok Terrible consequences DataInterpretability ! # $ # * ( # $ @ & # $ @ ^ @ % # @ % @ ^ # @ # $ * ( @ # ) $ # @ & # $ @ ^ # @ $ ) @ # $ # @ * # @ $#@####@@!! Read 3.5 B more… Privacy
- 163. Original image
- 164. Original image Correct segmentation Segmentation Road Cars Trees
- 165. Original image Correct segmentation Segmentation Road Cars Trees Original image Adversarial example
- 166. Original image Correct segmentation Segmentation Road Cars Trees Original image Adversarial example Altered image
- 167. Original image Correct segmentation Segmentation Road Cars Trees Original image Adversarial example Altered image New funny segmentation
- 173. Chart of possible reasons why deep learning may fail to revolutionise medicineLikelihood Effect UnlikelyHighlylikely Not nice, but ok Terrible consequences DataInterpretability ! # $ # * ( # $ @ & # $ @ ^ @ % # @ % @ ^ # @ # $ * ( @ # ) $ # @ & # $ @ ^ # @ $ ) @ # $ # @ * # @ $#@####@@!! Read 3.5 B more… Privacy Adversarial attacks
- 174. This is all great stuff, what is next?
- 175. kaggle.com
- 176. http://www.datasciencebowl.com/competitions/turning-machine-intelligence-against-lung-cance Team: Lauri Listak Supervisor: Dmytro Fishman Turning Machine Intelligence Against Lung Cancer
- 177. Turning Machine Intelligence Against Lung Cancer http://www.datasciencebowl.com/competitions/turning-machine-intelligence-against-lung-cance Team: Lauri Listak Supervisor: Dmytro Fishman
- 178. Turning Machine Intelligence Against Lung Cancer http://www.datasciencebowl.com/competitions/turning-machine-intelligence-against-lung-cance Team: Lauri Listak Supervisor: Dmytro Fishman 20% of lung cancer deaths can be reduced with early detection
- 179. High False Positives rates lead to interventional treatments, additional costs and patient anxiety 20% of lung cancer deaths can be reduced with early detection Turning Machine Intelligence Against Lung Cancer http://www.datasciencebowl.com/competitions/turning-machine-intelligence-against-lung-cance Team: Lauri Listak Supervisor: Dmytro Fishman
- 182. 2023
- 187. References • Series of blog posts “Do machines actually beat doctors?” by Luke Oakden-Rayner (https://lukeoakdenrayner.wordpress.com/ 2016/11/27/do-computers-already-outperform-doctors/) • Opportunities and obstacles for deep learning in biology and medicine by Ching et al. (http://www.biorxiv.org/content/biorxiv/ early/2017/05/28/142760.full.pdf) • Computational biology - deep learning by William Jones, Kaur Alasoo, Dmytro Fishman et al. (accepted)