I think a major reason for this is because of transfer learning. For computer vision, there are many good pretrained models that were trained on huge datasets (like ImageNet) that can be fine-tuned for custom tasks. Other fields often do not have such pretrained models and huge datasets to work on, so it turns out transforming a dataset into an image dataset and fine-tuning a pretrained model works better than training from scratch.
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Good question!
I think a major reason for this is because of transfer learning. For computer vision, there are many good pretrained models that were trained on huge datasets (like ImageNet) that can be fine-tuned for custom tasks. Other fields often do not have such pretrained models and huge datasets to work on, so it turns out transforming a dataset into an image dataset and fine-tuning a pretrained model works better than training from scratch.