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Every weak signal in fluorescence microscopy may contain critical biological information. However, the interference resilience required to detect such signals has traditionally relied on task-specific interference modeling, which limits generalizability. Here, we present a self-contrastive learning-based signal detection solution that achieves interference resilience without the need for interference modeling, thereby offering high generalizability. The method, DEPAF (deep pattern fitting), is a module that contrasts asynchronously generated data views from asymmetric model paths to extract signals from interference, while incorporating highly parallel signal recognition and localization in the process. In benchmark tests, we show that DEPAF improves the detection rate of ultra-high-density signals under low signal-to-noise ratio conditions by an order of magnitude. It is also compatible with, and substantially enhances the performance of various imaging techniques, such as super-resolution imaging, spatial transcriptomic imaging, and two-photon calcium imaging. DEPAF is expected to advance the signal-centric fluorescence microscopy techniques and inspire further advancements, especially in the era of image-based multi-omics.