I need some help regarding the selection of the sample size. I am working on medical image segmentation and I have to measure the accuracy of segmentation using the Jaccard index. The issue I am facing is that my data (images) collection is from different sources and for all of them, the ground truth is not available. I have five classes in total but I only have ground truth for three class. Even for these three classes, I have ground truth only for some of the images. What I found from different articles is that I have to select a sample size and on the basis of the sample size I can generalise for the whole dataset. For sample size selection, I need error rate and Confidence interval. Now my questions are: Q1) The standard confidence interval is usually 95 %, Is there any by the default value for error rate if don't have any idea what our error should be? Q2) Can I assume that if my segmentation technique is performing well on three classes, It will also work on other two classes for which I don't have ground truth? Based on statistics, what assumption can I make for full dataset based on my problem? Any guidance will be much appreciated.
Thanks in advance
Standard sample size selection techniques can be tried such as confidence intervals and error tolerance knowing that over sampling could be a disadvantage also
Since your images are coming from different modalities, it will be quite cumbersome to assume that an accurate segmentation method on one class would automatically extend to other classes (unlabeled). Try finding experts who can help you label some images.
you can use "image processing" and input to the Matlab. This discussion is related to " Vector Machin".
Vector Machin can help you .
please chek it...
https://www.mathworks.com/discovery/support-vector-machine.html?s_tid=srchtitle
@ Abbas Cheddad Thanks for your recommendation. However, it is not possible to find an expert in The UK. It then needs ethical approval and it will take a long time :(
Mohsen Mohebbi I have to to segmentation to extract the ROI first. Classification is the last step where SVM will used.
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