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Automatic Polyp and InstrumentSegmentation in MedAI-2021
Nordic Machine Intelligence
Polyp and instrument segmentation plays a vital role in the early diagnosis of colorectal cancer (CRC) in that physicians visually inspect the bowel with an endoscope to identify polyps. However, recent works only focus on the accuracy of prediction in the positive samples while omitting the False-Positive (FP) predictions in the negative samples that might mislead the physicians. Here, we propose a novel Dual Model Filtering (DMF) strategy, which efficiently removes FP predictions in negativedoi:10.5617/nmi.9125 fatcat:zri5ltdvlrb43hw7lphibaqatu