English: Simplified neural network example for object detection: As seen at the output at right, the network is trained to associate a small nucleus and finely granular chromatin with a benign cell, and to associate a large nucleus and coarsely granular chromatin with a [cancer cell. In this run, on an input image (left), the network correctly detects the benign cell. However, the weakly weighted association between fine chromatin and cancer cells also confers a weak signal to the latter from one of two intermediate nodes. In addition, a blood vessel (bottom left) that was not included in the training partially conforms to the patterns of large nuclei and coarse chromatin, and therefore results in weak signals for the cancer cell output. These weak signals may result in a false positive result for a cancer cell. In reality, textures and outlines would not be represented by single nodes, but rather by associated weight patterns of multiple nodes.
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Reference: Ferrie, C., & Kaiser, S. (2019) Neural Networks for Babies, Sourcebooks ISBN: 1492671207.
Simplified example of training a neural network in object detection: The network is trained by multiple images that are known to depict benign cells (upper left) and sea cancer cells (lower left), which are correlated with "nodes" that represent visual aspects, in this case nuclear size and chromatin pattern. The benign cells match with small nuclei and finely granular chromatin, whereas most cancer cells match with large nuclei and coarsely granular chromatin. However, the instance of a cancer cell with fine chromatin creates a weakly weighted association between them.
Subsequent run of the network on an input image (left): The network correctly detects the benign cell. However, the weakly weighted association between fine chromatin and cancer cells also confers a weak signal to the latter from one of two intermediate nodes. In addition, a blood vessel (bottom left) that was not included in the training partially conforms to the patterns of large nuclei and coarse chromatin, and therefore results in weak signals for the cancer cell output. These weak signals may result in a false positive result for a cancer cell.
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Simplified neural network example: The network is trained to associate small nuclei and fine chromatin with benign cells, and associate large nuclei and coarse chromatin with cancer cells. In this run, it correctly detects a benign cell at left