Deep Learning for Medical Image Processing

This content explores the application of deep learning in medical image processing, focusing on the development of point-of-care diagnostics using microscopes and smartphones. It discusses the challenges faced in microscopy diagnosis, the motivation behind the use of deep learning, hardware design considerations, dataset details, and the architecture of the deep convolutional neural network (CNN) for microscopy-based diagnostics.

• Deep Learning
• Medical Imaging
• Point-of-Care Diagnostics
• Convolutional Neural Network
• Healthcare

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1. DeepSci 2017 DEEP LEARNING FOR MEDICAL IMAGE PROCESSING Mr. R. Vinayakumar (Research Scholar) Mrs. V. Sowmya (Assistant Professor) Dr. K. P. Soman (Professor & Head) 11-11-2017.

2. DeepSci 2017 WHY DEEP LEARNING? DEEP LEARNING FOR MEDICAL IMAGE PROCESSING

3. DeepSci 2017 WHY DEEP LEARNING FOR MEDICAL DIAGNOSIS? Deep Learning is a Black Box, but Health Care Won t Mind New algorithms are able to diagnose disease as accurately as expert physicians -Monique Brouillette , MIT Technology Review , April 2017. DEEP LEARNING FOR MEDICAL IMAGE PROCESSING

4. DeepSci 2017 INTRODUCTION-MICROSCOPY DIAGNOSIS * Low-resources CHALLENGES: Skilled Technicians Critical Shortage CONSEQUENCES: error prone economic burden of buying unnecessary drugs DEEP CNN FOR MICROSCOPY BASED POINT OF CARE DIAGNOSTICS John A. Quinn et al., ICMLHC, 2016.

5. DeepSci 2017 MOTIVATION Common resources: Microscopes and smartphones. Automation with computer vision methods. DEEP CNN FOR MICROSCOPY BASED POINT OF CARE DIAGNOSTICS John A. Quinn et al., ICMLHC, 2016.

6. DeepSci 2017 OBJECTIVE Development of point-of-care (POC) diagnostics which utilize two relatively common resources: microscopes and smartphones. DEEP CNN FOR MICROSCOPY BASED POINT OF CARE DIAGNOSTICS John A. Quinn et al., ICMLHC, 2016.

7. DeepSci 2017 HARDWARE DESIGN Hardware Design DEEP CNN FOR MICROSCOPY BASED POINT OF CARE DIAGNOSTICS John A. Quinn et al., ICMLHC, 2016.

8. DeepSci 2017 DATASET Intestinal parasites (IP) Malaria (ML) Tuberculosis (TB) 3734 patches in 928 images 162 patches in 1217 images 7245 patches in 1182 images DEEP CNN FOR MICROSCOPY BASED POINT OF CARE DIAGNOSTICS John A. Quinn et al., ICMLHC, 2016.

9. DeepSci 2017 Architecture Input Convol 1 Max Pool 1 FC Convol 2 Output Filter 1 Filter 2 DEEP CNN FOR MICROSCOPY BASED POINT OF CARE DIAGNOSTICS John A. Quinn et al., ICMLHC, 2016.

10. DeepSci 2017 Architecture M x M x 3 C1 x C1 x N1 FC C2 x C2 x N2 P x P x N1 Output N1 - F1 x F1 x 3 N2 F2 x F2 x N1 P1 = C1/PS C1 = ( (M-F1+2Z)/S1)+1 C2 = ( (P-F2+2Z)/S2)+1 DEEP CNN FOR MICROSCOPY BASED POINT OF CARE DIAGNOSTICS John A. Quinn et al., ICMLHC, 2016.

11. DeepSci 2017 Architecture TB & ML 20 x 20 x 3 18 x 18 x 7 100 8 x 8 x 12 9 x 9 x 7 2 7 - 3 x 3 x 3 12 2 x 2 x 7 C1 = ( (20-3+0)/1)+1 P1 = 18/2 C2 = ( (9-2+0)/1)+1 DEEP CNN FOR MICROSCOPY BASED POINT OF CARE DIAGNOSTICS John A. Quinn et al., ICMLHC, 2016.

12. DeepSci 2017 CONVOLUTION 1 0 1 0 0 1 2 2 2 2 0 1 0 1 1 2 0 0 0 0 0 0 2 0 1 1 0 1 1 0 0 2 1 2 2 1 1 0 1 1 2 0 0 0 1 0 0 2 0 1 2 0 0 1 0 1 0 1 0 2 1 1 0 1 0 0 0 1 0 1 0 0 2 0 1 1 0 1 0 1 0 1 1 0 1 0 0 1 0 0 1 1 0 1 1 0 1 1 0 1 1 0 DEEP CNN FOR MICROSCOPY BASED POINT OF CARE DIAGNOSTICS John A. Quinn et al., ICMLHC, 2016.

13. DeepSci 2017 CONVOLUTION 2 1 1 0 0 0 1 0 0 1 0 1 1 0 1 0 0 2 0 1 1 1 1 1 1 1 1 1 0 1 0 0 1 2 1 2 2 1 2 1 0 2 0 0 0 0 0 0 2 0 1 0 0 2 0 1 1 0 1 1 0 0 2 1 2 2 1 1 0 1 1 2 0 0 0 1 1 2 3 0 0 2 0 1 0 1 1 1 0 1 0 1 0 0 1 0 0 1 1 1 0 1 0 1 0 0 1 1 0 1 0 1 1 3 1 0 2 2 2 2 2 2 2 3 1 0 0 4 0 DEEP CNN FOR MICROSCOPY BASED POINT OF CARE DIAGNOSTICS John A. Quinn et al., ICMLHC, 2016.

14. DeepSci 2017 CONVOLUTION 2 1 1 0 0 0 1 0 0 1 0 1 1 0 1 0 0 2 0 1 0 0 2 0 1 1 0 1 0 0 1 2 1 2 2 1 2 1 0 2 0 0 0 0 0 0 2 0 1 1 0 1 1 0 0 2 1 1 2 1 2 1 1 2 0 0 0 1 0 0 2 0 1 0 0 1 0 1 0 2 1 1 1 0 2 1 1 0 0 1 0 1 0 1 1 0 0 2 1 1 2 0 1 1 0 1 0 0 1 0 0 1 1 1 0 1 0 1 0 1 0 0 1 1 0 1 1 0 1 1 0 1 1 1 0 1 0 1 0 1 1 0 1 0 1 1 0 0 1 0 1 0 0 1 1 0 DEEP CNN FOR MICROSCOPY BASED POINT OF CARE DIAGNOSTICS John A. Quinn et al., ICMLHC, 2016.

15. DeepSci 2017 CONVOLUTION 5 2 5 1 1 4 3 3 2 2 1 2 1 1 1 3 2 2 2 2 2 3 1 1 0 0 1 1 1 0 4 4 3 1 0 1 1 0 1 0 2 1 1 1 0 2 1 1 0 0 1 0 1 0 1 1 0 0 2 1 1 2 0 C 1 0 1 1 1 0 0 1 I = 1 0 0 1 0 1 0 1 1 0 0 1 1 0 F 1 0 1 1 0 (9 1) (9 27) (27 1) CS231n: Convolutional Neural Networks for Visual Recognition. DEEP CNN FOR MICROSCOPY BASED POINT OF CARE DIAGNOSTICS John A. Quinn et al., ICMLHC, 2016.

16. DeepSci 2017 Architecture TB & ML 20 x 20 x 3 18 x 18 x 7 100 8 x 8 x 12 9 x 9 x 7 2 7 - 3 x 3 x 3 I = 12 2 x 2 x 7 1 (27 7) 1 C F (324 27) (324 7) = 2 (28 12) 1 C I F (64 12) (64 28) DEEP CNN FOR MICROSCOPY BASED POINT OF CARE DIAGNOSTICS John A. Quinn et al. ICMLHC, 2016.

17. DeepSci 2017 Architecture TB & ML CS231n: Convolutional Neural Networks for Visual Recognition. DEEP CNN FOR MICROSCOPY BASED POINT OF CARE DIAGNOSTICS John A. Quinn et al. ICMLHC, 2016.

18. DeepSci 2017 DATASET Test Intestinal parasites (IP) Malaria (ML) Tuberculosis (TB) Train & Test 50% + samples were augmented 7 times the original by flipping & rotating the patches - Samples 100 times the number of + samples. 2,61,345 patches (11.3% -> +) 3,15,142 patches (9.0% -> +) 2,53,503 patches (0.4% -> +) DEEP CNN FOR MICROSCOPY BASED POINT OF CARE DIAGNOSTICS John A. Quinn et al., ICMLHC, 2016.

19. DeepSci 2017 Sample Result-Malaria White box- labelled by an expert Red box- Detected by the machine DEEP CNN FOR MICROSCOPY BASED POINT OF CARE DIAGNOSTICS John A. Quinn et al. ICMLHC, 2016.

20. DeepSci 2017 Sample Result-Tuberculosis White box- labelled by an expert Red box- Detected by the machine DEEP CNN FOR MICROSCOPY BASED POINT OF CARE DIAGNOSTICS John A. Quinn et al. ICMLHC, 2016.

21. DeepSci 2017 Sample Result-Intestinal Parasites White box- labelled by an expert Red box- Detected by the machine DEEP CNN FOR MICROSCOPY BASED POINT OF CARE DIAGNOSTICS John A. Quinn et al. ICMLHC, 2016.

22. DeepSci 2017 Interesting Result-Intestinal Parasites DEEP CNN FOR MICROSCOPY BASED POINT OF CARE DIAGNOSTICS John A. Quinn et al. ICMLHC, 2016.

23. DeepSci 2017 Summary Better performance than the hand - engineered features Flexibility - Same network architecture Decision support tool Consistency in diagnosis DEEP CNN FOR MICROSCOPY BASED POINT OF CARE DIAGNOSTICS John A. Quinn et al. ICMLHC, 2016.

24. DeepSci 2017