UltraSeg-130K delivers Dice scores above 0.8 on seven polyp datasets at over 30 FPS on a single CPU core using 0.13M parameters, outperforming other sub-0.3M models and approaching larger networks on external tests.
Self-supervised feature learning via exploiting multi-modal data for retinal disease diagnosis.IEEE Transactions on Medical Imaging, 39(12):4023–4033, 2020
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Enabling Real-Time Colonoscopic Polyp Segmentation on Commodity CPUs via Ultra-Lightweight Architecture
UltraSeg-130K delivers Dice scores above 0.8 on seven polyp datasets at over 30 FPS on a single CPU core using 0.13M parameters, outperforming other sub-0.3M models and approaching larger networks on external tests.