The main challenge with medical data is still the access and ownership. ViT transformers looked good, but only when training on huge amounts of data. Another problem is the distribution of the data, having good results with an internal dataset of a hospital does no guarantee getting the same results with another hospital's dataset.
I didn't know about the HATNet mentioned in the article, but it looks an interesting paper to read.
There's a recent article[0] showing that a good training strategy with a ResNet can be better than novel architectures. From my experience, a ResNet-34 / ResNet-50 is usually enough (at least talking about classification), and the main bottleneck is your dataset, class imbalance, and handling out-of-distribution samples.
I totally agree but I’m excited for some recent initiatives to fix this. https://www.researchallofus.org/ This research project is open even to at home scientists although I think some of the genomic data may reasonably not be as open as other parts.
Unfortunately even if most people wanted to "donate" their medical data for research purposes, much of medical data (images especially) is locked into difficult to access systems.
I didn't know about the HATNet mentioned in the article, but it looks an interesting paper to read.
There's a recent article[0] showing that a good training strategy with a ResNet can be better than novel architectures. From my experience, a ResNet-34 / ResNet-50 is usually enough (at least talking about classification), and the main bottleneck is your dataset, class imbalance, and handling out-of-distribution samples.
[0] https://arxiv.org/abs/2103.07579