blackhole077 t1_j93xnn8 wrote on February 19, 2023 at 1:33 AM

Since I'm on a mobile device I'll write a shorter answer that hopefully gives you some insight.

From what I've understood of your question, you're wanting to know if bounding boxes would perform worse due to the proximity of cells you wish to detect.

Both methods may struggle with the cells being in close proximity, and instance segmentation may perform better in that regard. However I will reframe the question slightly.

First, there's a reason that object detection and instance segmentation are different methods. The latter is preferred in situations where you need to know the pixels that are considered to be the detected class, which I think is not what you're aiming for.

Second, the annotation process is, of course, more labor intensive when you want segmentation masks. Luckily you should be able to generate bounding boxes from masks easily, but keep it in mind if you're on a tighter schedule.

If you have additional questions please let me know. I wish you luck in your endeavor.

Hope this helps

Old_Scallion2173 OP t1_j945zye wrote on February 19, 2023 at 2:39 AM

thankyou for taking the time to answer my question. after reading your answer I've come to the conclusion that image segmentation can improve my model, but I am not using it for it's intended purpose, and also the fact that I have a lot of reading to do :). I do wish to ask tho, do you think I should instead focus on fine tuning my model and getting more dataset to improve the model? Maybe I'm getting too optimistic about instance segmentation.

blackhole077 t1_j96g3wh wrote on February 19, 2023 at 4:49 PM

> I do wish to ask tho, do you think I should instead focus on fine tuning my model and getting more dataset to improve the model? Maybe I'm getting too optimistic about instance segmentation.

I'm glad I've been of assistance. As for your follow-up question, it generally never hurts to have more data to work with and, of course, fine-tuning your existing models (if you have any at this time) can help as well.

I would say though, that you should determine what metrics you're wanting to see from your model first. As you mentioned earlier, you want to ensure that false negatives are as low as possible.

Naturally this translates to maximizing recall, which generally comes at the expense of precision. Thus, the question could be reframed as: "At X% recall how precise will the model be?" and "What parameters to the model can I tune to influence the precision at that recall?"

However, how false positives (FP) and false negatives (FN) and, by proxy, Precision and Recall, are defined is not as straightforward in object detection as it is in image classification.

Since I'm currently dealing with this problem, albeit in a different area altogether, here's a paper that I found useful for getting interpretable metrics:

https://arxiv.org/abs/2008.08115

This paper and its Github repository basically work on breaking down what exactly your model struggles with, as well as showing the FP/FN rates given your dataset. It might be a little unwieldy since it's a tool that has been somewhat neglected by its creator, but it's certainly worth looking into.

Hope this helps.