Hi,
I have one architecture that classifies the grade of muscle tear in an ultrasound image. I was going to create another model to train where the tear was located. The issue I am facing is there are multiple muscles in the ultrasound image and while it’s nice for it to label each, what I really need is it to recognize which muscle has the tear. What images do I show it for training- do I show it realistic images with more than one muscle or just the one muscle at a time? What do you recommend?
Here are a few things to consider when deciding whether to show your model realistic images with more than one muscle or just one muscle at a time:
The size of your dataset. If you have a large dataset of images with multiple muscles, then it may be beneficial to show your model realistic images with more than one muscle. This will help the model to learn to identify the different muscles in the image and to distinguish between them. However, if you have a small dataset of images, then it may be better to show your model images with just the one muscle at a time. This will help the model to learn to identify the muscle tear more accurately.
The complexity of the task. If the task of identifying the muscle tear is relatively simple, then it may be possible to show your model realistic images with more than one muscle. However, if the task is more complex, then it may be better to show your model images with just the one muscle at a time. This will help the model to focus on the muscle tear and to avoid getting distracted by the other muscles in the image.
The resources you have available. If you have the resources to collect and label a large dataset of images with multiple muscles, then this is the best option. However, if you do not have the resources to do this, then you may need to show your model images with just one muscle at a time.
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