HarDNet-MSEG: A Simple Encoder-Decoder Polyp Segmentation Neural Network that Achieves over 0.9 Mean Dice and 86 FPS

Chien-Hsiang Huang; Hung-Yu Wu; Youn-Long Lin

HarDNet-MSEG: A Simple Encoder-Decoder Polyp Segmentation Neural Network that Achieves over 0.9 Mean Dice and 86 FPS

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arxiv 2101.07172 v2 pith:IB6IDYKJ submitted 2021-01-18 cs.CV

HarDNet-MSEG: A Simple Encoder-Decoder Polyp Segmentation Neural Network that Achieves over 0.9 Mean Dice and 86 FPS

Chien-Hsiang Huang , Hung-Yu Wu , Youn-Long Lin This is my paper

classification cs.CV

keywords hardnet-msegdecodersegmentationachievesbackbonecalleddatasetsdetection

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We propose a new convolution neural network called HarDNet-MSEG for polyp segmentation. It achieves SOTA in both accuracy and inference speed on five popular datasets. For Kvasir-SEG, HarDNet-MSEG delivers 0.904 mean Dice running at 86.7 FPS on a GeForce RTX 2080 Ti GPU. It consists of a backbone and a decoder. The backbone is a low memory traffic CNN called HarDNet68, which has been successfully applied to various CV tasks including image classification, object detection, multi-object tracking and semantic segmentation, etc. The decoder part is inspired by the Cascaded Partial Decoder, known for fast and accurate salient object detection. We have evaluated HarDNet-MSEG using those five popular datasets. The code and all experiment details are available at Github. https://github.com/james128333/HarDNet-MSEG

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Cited by 2 Pith papers

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RPG-SAM: Reliability-Weighted Prototypes and Geometric Adaptive Threshold Selection for Training-Free One-Shot Polyp Segmentation
cs.CV 2026-03 unverdicted novelty 6.0

RPG-SAM improves one-shot polyp segmentation by weighting high-fidelity support features and dynamically adjusting thresholds via morphological consensus, yielding 5.56% mIoU gain on Kvasir.
QFedPolyp: A Communication- and Inference-Efficient Federated Learning Framework for Polyp Segmentation
cs.LG 2026-07 conditional novelty 4.0

Quantization-aware federated training with 8-bit parameter exchange cuts communication ~4x in polyp segmentation while keeping Dice within ~1.5 points of full precision.