You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
hello, Thanks for this code,
Actually, I'm interested in microcalcification detection using CNN algorithm.
Is it possible to modify your code so that i detect microcalcif in mammo ? if yes, How can i proceed plz !
Thanks,
The text was updated successfully, but these errors were encountered:
The approach is the same for any kind of lesion segmentation. Lesion segmentation meaning given a mammogram as input output an image of the same dimensions with 1s where the lesion is present and 0 everywhere else (or equivalently output an image of the same size with per-pixel probabilities of presence of lesion). The difference is that the mask used for training have to mark microcalcifications as positive and the rest as negative, (whether to mark both masses and microcalcifications as positive is a design decision). Once you've gotten a microcalcification database and generated the masks, you should be able to use code like this without change. In theory if you want to segment cats from no cats and have a database with cats mask, this still could be useful
hello, Thanks for this code,
Actually, I'm interested in microcalcification detection using CNN algorithm.
Is it possible to modify your code so that i detect microcalcif in mammo ? if yes, How can i proceed plz !
Thanks,
The text was updated successfully, but these errors were encountered: