How To Evaluate Image Segmentation Models

How To Evaluate Image Segmentation Models

How To Evaluate Image Segmentation Models. We are working on a deep learning model that predicts masks for brain tumors or skin lesions. What is making a mask? We classify pixels of an image as 1 or 0.

We are working on a deep learning model that predicts masks for brain tumors or skin lesions. What is making a mask? We classify pixels of an image as 1 or 0. If there is a mask in a pixel we state 1, if there is not a mask we state 0. Making pixelwise binary classification of images is called “Semantic Segmentation”.

If we are trying to recognize many objects in an image we are performing “Instance Segmentation”. Instance Segmentation is a multiclass segmentation. For example, in self-driving cars, objects are classified as car, road, tree, house, sky, pedestrian, etc.

In both semantic(binary) and instance (multiclass)segmentations, we need a loss function for calculating gradients.

Which accuracy-loss function is used for image segmentation?

Let’s see some of our options:

deep-learning data-science machine-learning medical image-classification

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