Sample models for caMicroscope.

Classification Models

The following sample classification models can be used by caMicroscope's predict application as-is. Classification models take in a square patch of the image in either RGB or black and white, and ouput a classification associated with the input. When using the model with caMicroscope, the patch size, classification labels, and whether the model operates on black and white or RGB images should be specified.

• ICIAR18 -- model and weights
• Bioimaging15 -- model, weights part 1, and weights part 2
• Lymphoma -- model, and weights

Segmentation Models

The following segmentation models can be used by caMicroscope's segmentation algorithm, as an alternative to traditional CV methods. Segmentation models take in image data and return a mask of the same size. When using the model with caMicroscope, the whether the model operates on black and white or RGB images should be specified.

• Mitosis -- model, weights part 1, and weights part 2.

• YNet -- model, weights part 1, and weights part 2.

Common Issues:

• Provided weight data has no target variable: This might be a common issue for the model to fail to load. To avoid this, make sure that the keras model is loaded exactly once. To make sure, reset the runtime and load the model before carrying to conversion. This case also goes when your converting using the bash command. The Keras model which you saved must be loaded just once. To make sure, save the model, reset the runtime, load the model and save it again. Multiple loads seem to mess up the layer names. You can track this issue further here.
• Don't use Keras' Lambda in model definition. If want to use, save weights, remove Lambda calls, load weights again and then save the complete model as .hdf5

Contributing

Feel free to contribute:).