I'm working on a paper and would love some input on model choice. Suppose you're trying to detect a specific type of cancer, but the negative samples are visually and morphologically very similar (i.e., “mimics” of the cancer). In this setting, would it make more sense to approach the problem as: Anomaly detection (treating the cancer as the target distribution and everything else as out-of-distribution), or Supervised classification (explicitly learning to distinguish cancer vs. mimics)? submitted by /u/DryHat3296 [link] [Kommentare]