Hey all,
I’m working on an RL model to control the Z stage for automatic focus. The current method used in the OpenFlexure app is pretty good, I’m just investigating this approach to see if it can converge quicker on an optimal focus point. Anyway, just curious if anyone else is interested in this and, if you are, if you’d be interested in helping me build a dataset.
I currently have a few Z stacks, 20 steps apart, 100 images per stack, of a blood and kidney slide. The dataset is here: Microscope Focus Dataset | Kaggle but I’m interested in getting a more diverse training data to make the model more general. This is where I’m curious if anyone had an interest in making the dataset larger.
The technical approach I’m taking is based on this paper: https://arxiv.org/pdf/1809.03314, except I may make a few superficial changes to the network.
Update 27-APR-2024: Since starting this problem, I’ve found that RL-based training is probably not appropriate, even though its been used before for this type of problem in research. This is not an RL-problem, since the optimal action the agent may choose to perform in order to bring the slide into focus, during training, is easily determined by the dataset. After considering this, I have found that the following paper is a better basis for a learning-based auto focusing agent: Rapid Whole Slide Imaging via Dual-Shot Deep Autofocusing | IEEE Journals & Magazine | IEEE Xplore
Update 28-APR-2024: With some additional slides coming in the mail that will provide a more diverse set of training images, I’ve decided to make a simple labeling tool to help speed up the labeling process. I haven’t found a labeling tool that streamlines this specific workflow very well, compared to labeling workflows to object detection, segmentation, etc. I’ll be releasing a new version of the dataset I linked earlier that contains more sample types other than blood cells and kidney cells, a larger Z range, and an easier dataset format to work with.