Abstract
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 negative samples with metrics based threshold setting. To better adapt high-resolution input with various distributions, we embed the PVTv2 backbone to the framework SINetV2 as our model since the polyp segmentation is one of the downstream tasks of camouflaged object detection (COD). Experiments on challenging MedAI datasets demonstrate our method achieves excellent performance. We also conduct extensive experiments to study the effectiveness of the DMF.
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