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I also have found the same problem, the solution till I have figured out is to change the image resolution below 500px of width sorts of solving the issue as well as needed to change the sigma value inside apply_canny()
function. By doing this changes, I could have removed pectoral muscles of 70% of my dataset. 30% still gives IndexError: list index out of range.
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i face the same problem with mini mias,any one find the solution for it?
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This problem is still not solved. So I used a different approach to my dataset where I split it into grids and only select those grids of the image which has a tumour or segmented ground truth. So through this way, I bypass this process and it helps me to decrease image dimensions as well.
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Can I know how you split to grid and get tumor or ground truth ?I try to use threshold segmented but not work and i use mini mias dataset
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sorry for being late. so basically you can do it by your choice, lets i choose a grid of 500x500 so now i start taking first grid then adding stride of 500 i keep convolving and divide the image. so after that i only select the ones in which pixel values are 1 otherwise i discard. similarly i do the same with the original feature image where i consider the same grid size and select the one where there exist 1 in corresponding ground truth.
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