1. A STUDY OF X-RAY IMAGE PERCEPTION FOR PNEUMOCONIOSIS DETECTION MS Thesis Presentation Varun Jampani (200502027) varunjampani@research.iiit.ac.in Adviser: Prof. Jayanthi Sivaswamy Center for Visual Information Technology IIIT-Hyderabad

2. INTRODUCTION

3. Medical Imaging Technologies Forms one of the most effective diagnostic tools in medicine Used for planning treatment and surgery Several imaging technologies: PET, MRI, X-ray, Nuclear medicine, Ultrasound etc… X-ray is still ubiquitous in clinical practice and will likely remain so for quite some time

4. Perception in medical imaging Information in medical images itself not sufficient Information has to be interpreted in accurate and timely manner Sever factors affect reading medical images Observer independent factors such as image quality and viewing settings Observer dependent perceptual and cognitive factors The present work deals with understanding of some perceptual and cognitive factors involved in the diagnostic assessment of Pneumoconiosis.

5. Pneumoconiosis Inflammation of lungs Caused by prolonged inhalation of industrial dust [Mason and Broaddus 2005] Formation of scar tissue making lungs less flexible and porous Symptoms Shortness of breath, cough, restless sleep, chest discomfort. Mainly diagnosed through chest x-rays Effective way to prevent progress of this disease is to get regular check ups Deemed to be the most common and serious occupational lung disease in developing countries like China and India [Wang and Christiani 2003]

6. Diagnosis of Pneumoconiosis Complex process and requires certain level of expertize [Morgan et al. 1973] International labor organization (ILO) classification scheme [IRPAINIR Committee 1980] Hierarchy of readers Each lung is divided into 3 zones – Total 6 zones Profusion level (concentration of small opacities) is assessed for each zone

7. Importance of Perception Research Radiologist’s interpretation of medical images is highly subjective Inter and Intra observer variations [Krupinski 2000] At least half of errors made in clinical practice are perceptual in nature [Krupinski et al. 1998] The ultimate aim of all perception research is to improve diagnostic accuracy by reducing errors due to perceptual and cognitive factors Understanding perceptual factors helps in development of Better image acquisition and viewing systems Computer aided diagnostic (CAD) tools Better training regimens for resident radiologists

8. Objectives of present work We are interested in answering the following research questions 1. What is the role of expertize and contralateral symmetry information present in chest x-rays on the diagnostic error, time and eye movements of the observer? 2. Does the distribution of eye fixations change with observer error and observer assessment of pneumoconiosis? 3. What is the inter observer and intra observer variability of eye fixations? 4. What is the role of anatomical features in attracting the gaze of the observers? 5. What is the role of bottom up image features in attracting the gaze of the observers? 6. How do the visual strategies of the observers of different expertize levels change with time?

9. METHODOLOGY

10. Methodology Eye tracking experiment is chosen method of study Experiments were conducted in a room dedicated to eye tracking experiments Experimentally manipulated variables Expertize Contralateral symmetry Disease level Recorded variables Profusion categorization for each lung zone Time of analysis Eye fixations

11. Experimental Images Good quality PA chest x-ray images of pneumoconiosis Single and double lung images to study the role of contralateral symmetry Different disease stages Disease Stage Double Lung Images Single Lung Images Stage 132 Stage 246 Stage 3108 Total: 17Total: 16

12. Participant (observer) details Expertize varying from novices to staff radiologists Total number of observers: 23 Observer CategoryNumber Novices3 1 st year medical students3 2 nd year medical students3 3 rd year medical students3 4 th year medical students3 Resident Radiologists4 Staff Radiologists4

13. Eye tracker settings Remote head free eye tracker Model: SR Research Eyelink 1000 Mean spatial accuracy of eye tracker is 0.5 0 visual angle and sampling rate was 500 Hz. 17 inch LCD monitor Approximate distance between the observer and the screen was around 60 cm

14. Experimental Procedure With-in subject design Steps in experiment 1. Consent form 2. Training session 3. Cover story 4. 9-point camera calibration 5. All 33 experimental images were shown one after other to each observer Unlimited viewing time After viewing is done, observer has to note down profusion level for each lung zone in the report form given to him/her Eye-movement data, response times and profusion levels were recorded for each observer and for each image

15. Eye movement terminology TermMeaning FixationsPoints on the image where observers look (fixate) SaccadesStraight line paths between different fixations SaliencyLikelihood of an image location to be fixated Saliency mapImage with each pixel value representing saliency at that location Heat mapSaliency map overlaid onto the original image

16. Sample saccade maps

17. ROLE OF EXPERTIZE AND CONTRALATERAL SYMMETRY

18. Research Questions 1. What is the role of expertize and contralateral symmetry information on the diagnostic error, time and eye movements of the observer? 2. Does the distribution of eye fixations change with observer error and observer assessment of pneumoconiosis?

19. Some existing results on pneumoconiosis diagnosis Image quality plays serious and significant role [Pearson et al. 1965, Reger et al. 1972] Marked tendency to award higher readings to under-penetrated films while the opposite is true for over-penetrated films Experienced radiologists are more able to adjust for unsatisfactory film quality Substantial inter-reader and some intra-reader variation in assessment of pneumoconiosis [Reger et al. 1972, Kruger et al. 1974] Experienced radiologists have more consistency No eye tracking experiments on pneumoconiosis

20. Contralateral symmetry in chest x-rays Detection of symmetry is one of the characteristics of human visual perception Important role in face perception [Chen et al. 2007] and attractiveness [Grammer et al. 1994] Contralateral subtraction technique Proved to be useful for highlighting tumor regions in chest x-rays [Tsukuda et al. 2002] Also used in computer aided analysis of tumors in chest x-rays [Li et al. 2000] No empirical studies on the role of contralateral symmetry (CS) in diagnosing lung diseases. Source: Tsukuda et al. 2002

21. Analysis of observer performance Sum of absolute differences Observer error for each observer is obtained by averaging over all images

22. Average sum of absolute differences

23. Analysis Observer error for double lung images is seen to vary significantly with expertise, which was confirmed by Kruskal-wallis test (χ 2 (6) = 13.38, p =.038) Thus, there is decrease in error with increase in expertize The observer error for single lung images (Mdn = 0.813) is significantly higher than that of double lung images (Mdn = 0.620). (wilcoxon signed rank test: Z = 3.13, p <.001) Significant difference between doctors (Mdn = 0.38) and non-doctors (Mdn = 0.18) when considering the difference of observer error between single and double lung images. (Mann-Whitney test: U = 28, p =.038) Doctors: residents and staff Non-Doctors: Other groups

24. Penalize Over and Penalize Under Penalize Over: Number of times an observer has given a profusion rating higher than that of ground truth. Penalize Under: Number of times an observer has given a profusion rating lower than that of ground truth. Over-estimation and Under-estimation

25. Penalize over More penalize over in double lung images (Mdn = 0.31) than in single lung images (Mdn = 0.25) (Wilcoxon signed rank test: Z = 2.13, p =.033)

26. Penalize Under More penalize under in single lung images (Mdn = 0.34) than in double lung images (Mdn = 0.28) (Z = 3.89, p <.001) Thus CS information is helping in not under-estimating profusion values

27. Inferences CS plays a significant role in the diagnosis of pneumoconiosis and its role is more important in doctors than in the case of non-doctors. A previous study [Rockoff and Schwartz 1988] on underestimation of asbestosis (a variant of pneumoconiosis) Thus, CS information helps by reducing the tendency of giving less profusion ratings More experiments required to study at what level (image/zonal/local) this CS information is being used

28. Time Analysis Doctors (Mdn = 16.69s) took less time than non-doctors (Mdn = 33.24s) (Mann-Whitney test: U = 21, p =.011) Time taken for double lung images (Mdn = 30.84s) is less than double the time taken for single lung images (Mdn = 38.38s) (Wilcoxon signed rank test: Z = 4.19, p <.001)

29. Eye fixation analysis Average saccade velocity of doctors is significantly higher than that of non-doctors (Mann-Whitney test: U = 20, p =.01) Average saccade amplitude is also significantly higher for doctors than that of non-doctors (U = 24, p =.022) Doctors seem to be moving eyes more quickly and over more distances compared to non-doctors.

30. Fixations vs. observer ratings Average percentage fixation time in lung zones with observer ratings of 1 and 2 is significantly higher than in the lung zones with observer ratings of 0 and 3, in both single and double lung images (Mann-Whitney test: p <.001). Zones considered by the observer as definite normal (profusion rating – 0) and definite abnormal (3), are less viewed when compared to that of other zones

31. Fixations vs. observer error Significantly less time is spent on those zones with absolute error of 3 when compared to that of zones with absolute errors of 0,1 and 2. This shows the importance of careful analysis of each lung zone

32. Inferences Doctors are quick and efficient where as non-doctors are slow and inefficient For good diagnostic results, all zones should be looked carefully. X-rays should not be speed read Some of these results may not be applicable to x-rays of localized lung diseases such as lung cancer etc.

33. What attracts observer’s eyes while reading chest x-rays of pneumoconiosis?

34. Objectives To get some insights into the factors guiding the attention of the observers with different expertize levels We mainly concentrate on the study of the role of anatomical features and bottom-up saliency in guiding the fixations of the observers Long term goal: Develop a system which predicts fixations of observers on a given chest x-ray Can be done by analyzing the image features underlying the fixation points of radiologists.

35. Visual attention Visual Attention: Process of selectively attending to an area of visual field while ignoring the surrounding visual areas Mostly done by actively moving eyes over the visual scene The eye movement control is mostly unconscious In general, where radiologists attend to in medical images differs from what they think they have attended to

36. Top down and bottom up influences Bottom up influences Dependent on the features of visual stimulus Independent of the observer Stimulus driven or exogenous attention Top down influences Image independent factors such as given task or goal and knowledge of the observer Goal-driven or endogenous attention Eye movement recordings of an observer over a picture while performing different tasks (source: Yarbus 1967)

37. Feature Integration Theory* One of the most influential theories of visual attention Two stages in object perception 1. Pre-attentive stage: Object is analyzed in terms of its different features such as color, orientation etc., which are processed in different areas of the brain. 2. Focused attention stage: This stage integrates different features in order to perceive the object as a whole. Attention is responsible for binding various features of an object to perceive or recognize whole object Information from different feature maps are collected in a master map (also called saliency map) * Triesman and Gelade 1980

38. Computational models of visual attention Provides computational details of the process of visual attention Many existing models are biologically motivated Output of any computational visual attention system is saliency map Many computational models have been proposed Most are bottom-up models

39. Itti-Koch Model Source: Itti and Koch 2000

40. Eye tracking research on chest x-rays Most work done on chest x-rays of localized lung diseases like tumors Large areas in chest x-rays are not sampled by fixated [Kundel 2000] Radiologists move eyes in a pattern that is neither random nor the same as that of a layman [Kundel and Wright 1969] Evolution of fixation pattern from that of an untrained person to that of a radiologist [Kundel and La Follete 1972]

41. Global focal model of visual search 3 main components Overall pattern recognition Focal attention to image detail Decision making Initial global response involving entire retina followed by a series of checking fixations Source: Nodine et al. 1987

42. Data Analysis in present study Only first 80 fixations are considered for each observer Non-parametric statistical tests are used as most data didn’t pass the Normality test P-values less than 0.05 are considered significant Two tailed p-values are considered whenever two groups are compared Expertize GroupMean number of fixations Novices113.02 Medical Students87.99 Resident Radiologists62.26 Staff Radiologists47.66 Total Mean : 79.77

43. ROC analysis for comparing saliency maps with fixations An ROC metric is used to evaluate the performance of saliency maps to predict eye fixations Saliency map from the fixation locations of one observer is treated as a binary classifier on every pixel in the image Saliency maps are thresholded such that given percentage of image pixels are classified as fixated and the rest are classified as not fixated. The fixations from remaining observers are treated as ground truth Standard approach used in eye tracking studies

44. Thresholded saliency maps Top: A saliency map Right: Corresponding saliency map thresholded to different percentage of pixels 10% salient 20% 80% 90%30% 70% 40% 50% 60%

45. ROC analysis ROC curve is drawn by varying the threshold Area under ROC curve indicates how well the saliency map of one observer can predict the ground truth fixations (fixations of remaining observers) The more the ROC area, the better is the predicting capability of the observer saliency map For perfect classifier: ROC area – 100 For random classifier: ROC area - 50

46. An example ROC curve 33% fixations in top 10% salient regions 58% fixations in top 20% salient regions 74% fixations in top 30% salient regions 85% fixations in top 40% salient regions 92% fixations in top 50% salient regions 96% fixations in top 60% salient regions 98% fixations in top 70% salient regions 100% fixations in top 80% salient regions Average AUC = 79.28 (fair accuracy)

47. Inter observer fixation consistency One of the aims is to automatically detect the areas of interest for the radiologists A basic assumption behind this is that all the observers would look at similar locations in a given image This assumption should be validated Does different observers fixate at same locations, in a given chest x-ray?

48. Previous research It has been shown [Kundel 2008] that, while detecting lung nodules, even though different observers have different scan paths, the distribution of their eye fixations is similar [Judd et al. 2009] found the good consistency between the eye fixations of different observers while free viewing the natural images Source: Kundel 2008

49. Human Saliency Maps Fixation maps are convolved with Gaussian to get human saliency maps Fixation points with more duration are more emphasized Human saliency map of an observer: Fixation map convolved with Gaussians; and saliency map overlayed on the original chest x-ray

50. Results: Inter-observer consistency Median AUC for all observers: 79 (reasonably good accuracy) More agreement in fixations among the observers of lower expertize groups than that of higher expertize groups Thus more common factors guiding the fixations of lower expertize groups

51. Intra-observer fixation consistency Since all images are PA chest x-rays, we can expect some intra observer fixation consistency also What is the consistency of eye fixations of an observer while diagnosing pneumoconiosis? Does an observer fixate at same locations, in different x-ray images?

52. Results: Intra-observer consistency Median AUC for all the observers is 80.1 AUCs seems to be decreasing with increasing expertize

53. Inter vs. Intra-observer fixation consistency AUCs corresponding to intra- observer analysis (Mdn = 80.1) are significantly higher than those corresponding to inter-observer analysis (Mdn = 79) (Wilcoxon signed rank test: Z = 29.5, p <.001) Observer reading style and non-image specific features such as anatomical features seem to be playing more role in guiding the fixations of observers

54. Remarks Reasonably good consistency of both inter and intra observer eye fixations Thus eye fixations of an observer can indicate important regions in an image and helps in predicting the eye fixations of other observers Higher expertize groups seem to have divergent image- specific visual strategies compared to that of lower expertize groups

55. Role of image features in predicting fixations Aim is to find image features which can be used to predict fixations of observers Image features Low-level: pixel intensity, color, orientation etc. Mid-level: blobs, holes etc. High-level: anatomical structures such as ribs, heart etc. Features studied in present study High-level anatomical features Low-level bottom-up image features

56. Role of anatomical features Aim is to determine the role of anatomical features in attracting the gaze of the observers Anatomical features considered Lung, rib and inter-rib regions Top down knowledge

57. Regions of Interest Lung region and inter-rib regions are generally considered regions of interest for radiologists Not yet studied whether observers really look at these regions more

58. Segmentation of lungs and ribs We used Euler number based thresholding [Wong and Ewe 2005] to get roughly segmented lung regions, and then we used an active contour method, similar to the approach in [Annangi et al. 2010], to finely segment lung regions Ribs are manually segmented as existing methods are not good enough

59. Anatomical distribution of fixations Fixation density in different anatomical regions for different expertize groups Different regions have different areas Thus, normalize fixation percentages with respect to area

60. Results and Observations Fixation density in lung regions is significantly well above the fixation density of entire image No significant relation between expertize levels and fixation density of different anatomical regions Both inter-rib and rib regions are fixated almost equally Anatomical left regions have more fixation density compared to anatomical right regions Fixation density in different anatomical regions for different expertize groups

61. Remarks Thus, lung regions are given more importance as expected Contrary to popular belief of importance to inter-rib regions, rib regions are also given same importance Reason for left anatomical region dominance is yet to be studied

62. ROC analysis using anatomical saliency maps Anatomical saliency maps Saliency maps with more saliency over the corresponding anatomical regions ROC analysis considering anatomical saliency maps as classifiers and observer fixations as ground truth Useful for comparisons with inter and intra observer fixation consistencies Saliency over Inter-rib regions Saliency over Rib regions Saliency maps overlayed onto the original x-ray images

63. Thresholded anatomical saliency maps Rib saliency map: More saliency on ribs Inter-rib saliency map: More saliency on inter-rib regions Random lung saliency map: random saliency inside lung regions A sample x-ray and corresponding rib, inter-rib and random lung saliency maps thresholded to different percentage of pixels

64. Results Wilcoxon signed rank test showed no significant difference between AUCs corresponding to rib, inter-rib and random lung saliency maps In addition, there is a good correlation between AUCs related to rib, inter-rib and random lung saliency maps Thus ribs and inter-rib regions are given equal importance and same importance as any other random point inside lung region

65. Comparison with fixation consistencies Thus, much of fixation consistency can be explained by the importance given to lung regions Still significant difference between AUCs of random lung saliency maps and that of inter observer fixation consistency Median AUC of 74.3 for random lung saliency maps shows good role of lung regions in attracting gaze AUC for inter-observer fixation consistency = 79.0

66. Role of bottom-up saliency Bottom-up saliency: Saliency due to image dependent and observer independent factors Several computational bottom-up saliency models in literature We used 10 state-of-art models in present study What is the role of bottom-up saliency in attracting the gaze of the observers?

67. Previous Research Several studies have shown the importance of bottom-up saliency in guiding the visual attention of observers while viewing natural images. A recent study [Matsumoto et al. 2011] on brain CT images shows the importance of bottom-up saliency in attracting the gaze of neurologists

68. Saliency models considered 1. Itti-Koch Saliency model (IK) [Itti and Koch 2000] 2. Graph based visual saliency (GBVS) [Harel et al. 2006] 3. Image Signature (SIG) [Hou et al. 2012] 4. Spectral residual approach (SR) [Hou et al. 2007] 5. Dynamic visual attention (DVA) [Hou et al. 2008] 6. Adaptive whitening saliency (AWS) [Garcia-Diaz et al. 2009] 7. Attention based on information maximization (AIM) [Bruce and Tsotsos 2009] 8. Saliency based on local self-resemblance (SDSRL) [Seo and Milanfar 2009] 9. Saliency based on global self-resemblance (SDSRG) [Seo and Milanfar 2009] 10. Context-aware saliency (CA) [Goferman et al. 2010]

69. BU saliency maps of a chest x-ray Original image and corresponding bottom-up saliency maps (thresholded to different percentage of pixels)

70. ROC Analysis With saliency maps as classifiers and observer fixations as ground truth GBVS and SIG models significantly outperform other saliency models AUCs related to GBVS (Mdn = 77.1) are significantly higher than those of SIG saliency maps (Mdn = 73.8) (Wilcoxon signed rank test: Z = 2.0, p <.001)

71. Comparisons

72. Results AUCs for GBVS saliency maps are significantly higher than those of random lung saliency maps (Wilcoxon signed rank test: Z = 13.0, p <.001) The difference between GBVS and inter-observer saliency map AUCs is not significant (p =.0162) Thus, GBVS explains most of the observed inter-observer fixation consistency We can say that bottom-up saliency is an important factor in guiding the eye fixations of the observers.

73. Effects of Time How do the inter-observer fixation consistency, intra- observer fixation consistency, role of lung regions and role of bottom-up saliency (GBVS) change with time (number of fixations)? Would give more insights into the visual strategies used by the observers

74. Effects of Time Inter-Observer Fixation ConsistencyIntra-Observer Fixation Consistency Role of Random lung saliency mapsRole of bottom up saliency

75. Inter-Observer fixation consistency Initially high, decreases to about 20 fixations and then increases slowly to plateau off Similar regions are attended during first few fixations First few fixations seem to be playing role in determining the later visual strategies More true in case of doctors

76. Intra-Observer fixation consistency Similar trend to that of inter-observer consistency Doctors seem to be using same visual strategies initially Doctors strategies seem to diverge after initial few fixations Visual strategies of non-doctors doesn’t seem to diverge as much

77. Role of random lung saliency maps No clear trend in the AUCs as the fixation number increases except in case of novices Role of lung regions in attracting gaze seems to be consistent over entire viewing period

78. Role of bottom-up saliency Except in the case of staff radiologists, bottom up saliency seem to be playing more role in attracting the gaze of the observers during the initial fixations when compared to the later ones

79. Concluding Remarks First few fixations seems to be playing important role in choosing the visual strategy, appropriate for the given image Experience seems to be helping observers to develop new visual strategies based on the image content so that they can quickly and efficiently assess the disease level Bottom-up saliency (GBVS) is shown to play an important role in attracting the gaze Lung regions attract most of the attention Whereas, the role of bottom-up influences is more during the initial few fixations, the role of top-down influence seems to be more during the latter part of the viewing.

80. TOWARDS A NEW SALIENCY MODEL

81. Objective To develop a gaze prediction system for chest x-rays of Pneumoconiosis Useful for the development of CAD systems Training tools for radiologists Based on experimental results we developed a new saliency model by extending GBVS saliency model

82. A new saliency model Bottom-up saliency map Extracted using GBVS saliency model Top-Down saliency map Importance given to lung regions Both maps are combined with simple multiplication as in [Peters and Itti 2007] We call resulting saliency model as ‘Extended Graph based Visual Saliecy’ (EGBVS)

83. Extended graph based Visual Saliency

84. Sample EGBVS saliency maps

85. Assessment of proposed model Used same ROC analysis as earlier

86. Results From the above chart, it can be seen that the median ROC areas for EGBVS model are higher than those of GBVS model, for all the participants Wilcoxon signed rank test showed that the ROC areas for EGBVS (Mdn=81.3) are significantly higher (Z=2.0, p<0.001) than those of GBVS (Mdn=77.1), for all the observers No significant differences of EGBVS AUCs across different expertize groups In other words, AGBVS model performs significantly better than GBVS model in predicting the eye fixations of the observers

87. Comparisons with fixation consistency

88. Results AUCs related to EGBVS (Mdn = 81.3) are significantly higher than those related to inter-observer fixation consistency (Mdn = 79) (Wilcoxon signed rank test: Z = 18.3, p <.001) No significant difference between AUCs related to EGBVS and those related to intra-observer fixation consistency Thus, EGBVS saliency maps performs significantly better than (2.9% increase in AUC) human saliency maps (of other observers) in predicting the eye fixations of the observers

89. Concluding Remarks Simply modifying GBVS saliency maps with importance to lung regions resulted in significant improvement in accuracy EGBVS saliency model seems to explain the observed inter-observer fixation consistency completely Need to incorporate other top-down influences such as influences specific to expertize etc…

90. CONCLUSION AND FUTURE DIRECTIONS

91. Main conclusions in present work Expertize and CS seems to play an important role in diagnosis of pneumoconiosis CS seems to be helping in reducing the general tendency of giving less profusion ratings Despite being specialized task, the bottom-up saliency seems to be playing important role in attracting fixations Lung regions attract most the attention Lower expertize groups seem to be using same visual strategies independent of image content Higher expertize groups are able to develop different visual strategies depending on the image content, so that they can quickly and efficiently assess the disease level

92. Future Directions Understanding the level at which the CS information is helping observers Studying the role of other top-down influences Finding the relative roles of bottom-up and top-down influences Study if the present results extend to diagnosing localized lung diseases such tumors Incorporating these results in developing CAD tool for pneumoconiosis Long term goals would be to develop an automated diagnostic system for pneumoconiosis A assistive system for radiologists based on eye tracking

93. Related Publications / Conferences V. Jampani, Ujjwal & J. Sivaswamy, Assessment of Computational Visual Attention Models on Medical Images. Indian Conference on Vision, Graphics and Image Processing, Mumbai, India, Dec. 2012. (to be published) V. Jampani, V. Vaidya, J. Sivaswamy and L. T. Kishore. Role of expertize and contralateral symmetry in the diagnosis of pneumoconiosis: an experimental study, Proc. of SPIE 2011, Vol. 7966, March 2011 V. Jampani, V. Vaidya, J. Sivaswamy, P. Ajemba and L. T. Kishore, Effect of expertise and contralateral symmetry on the eye movements of observers while diagnosing pneumoconiosis, Medical Image Perception Society Conference, Dublin, August 2011 (MIPS Student Scholar)

94. Acknowledgments Special Thanks to Prof. Jayanthi Sivaswamy (my adivsor) Prof. Bipin Indurkhya (played important part in my research) Vivek Vaidya (helped in data analysis) Peter Ajemba (helped in data analysis) GE Global Research, Bangalore (for funding) Dr. Kishore Taurani (for helpful discussions) Radiologists at CARE Hospital Hyderabad (for participating in eye tracking experiments) Students of Osmania Medical College, Hyderabad (for participating in eye tracking experiments) To my friends, parents and brother

95. THANK YOU Questions and Suggestions are welcome… Varun Jampani varunjampani@research.iiit.ac.in