I'm currently working on a project that requires the detection of duplicate bands in Western blot images. The task involves two types of duplicates:
Duplicates within the same Western blot image (intra-blot duplicates) Duplicates between different Western blot images (inter-blot duplicates)
I've amassed a collection of retracted papers with Western blot images, some of which contain duplicates, and others which do not. My goal is to create an annotated dataset from these images that can be used to train a deep-learning model for this specific task.
For intra-blot duplicates, my current approach is to annotate bounding boxes around each pair of duplicate bands within the same blot (like is shown in the image below). However, I'm unsure about the best way to annotate inter-blot duplicates, as this would seem to require some method of linking or correlating bands across different images.
One approach I have thought of is creating something like a CSV file like the one below;
Here, each row corresponds to a single object within an image. The
'object_id' field identifies the object, and the
'group_id' field indicates which other objects it's a duplicate of (i.e., all objects with the same group_id are considered duplicates). The
'y_min' fields specify the coordinates of the top left corner of the bounding box for the object, and the
'height' fields specify the size of the box.
This is my first time creating a dataset, so I'm seeking guidance on the most effective way to annotate and structure it, particularly concerning inter-blot duplicates.
Any insight or advice would be greatly appreciated.