However, in our initial studies, we found that introducing the original self-attention mechanism to the VPS task does not achieve satisfactory results (i.e., high accuracy and speed). 2252255 (2019), Krhenbhl, P., Koltun, V.: Efficient inference in fully connected CRFs with gaussian edge potentials. Ling Shao. 311. Existing polyp segmentation methods are mostly based on convolutional neural networks (CNNs), which usually utilize the global features to enhance We propose Uncertainty Augmented Context Attention network (UACANet) for polyp segmentation which considers an uncertain area of the saliency map. I think the arrow of X is opposite in the magnified illustration of NS. Detailed quantitative, qualitative and ablative studies are given on 3 datasets. Effectiveness of the Number of NS Blocks. In this paper, we propose the novel PNS-Net (Progressively Normalized Self-attention Network), which can efficiently learn representations from polyp videos with real-time speed (~140fps) on a single RTX 2080 GPU and no post-processing. Existing video polyp segmentation (VPS) models typically employ convolutional neural networks (CNNs) to extract features. Note: If we miss some treasure works, please let me know via e-mail or directly push a PR. Our basic normalized self-attention blocks can be easily plugged into existing CNN-based architectures. MHSA-Net: Multi-Head Self-Attention Network for Occluded - DeepAI Analysis. The object-level segmentation masks in SUN-SEG dataset of different pathological categories, which is densely annotated with experienced annotators and verified by colonoscopy-related researchers to ensure the quality of the proposed dataset. IEEE TMI 33(7), 14881502 (2014), Murugesan, B., Sarveswaran, K., Shankaranarayana, S.M., Ram, K., Joseph, J., Sivaprakasam, M.: Psi-Net: shape and boundary aware joint multi-task deep network for medical image segmentation. In: MICAD, vol. To fully utilize the temporal and spatial cues, we propose a simple normalized self-attention (NS) block. 285294. We set \(H^{l}=\frac{H'}{4}\), \(W^{l}=\frac{W'}{4}\), \(C^{l}=24\), \(H^{h}=\frac{H'}{8}\), \(W^{h}=\frac{W'}{8}\), and \(C^{h}=32\). After you download all the pre-trained model and testing dataset, to accurately segment polyps from colonoscopy videos with super high speed (140fps). Code: http://dpfan.net/pranet/(VPS) models typically employ convolutional neural networks (CNNs) to extract features. Rather than computing the response between a query position and the feature at all positions, as done in[wang2018non], the relevance measuring block can capture more relevance However, due to their limited receptive fields, CNNs cannot fully exploit the global temporal and spatial information in successive video frames, resulting in false positive segmentation results. 72237226 (2019), Puyal, J.G.B., et al. PubMedGoogle Scholar, Erasmus MC - University Medical Center Rotterdam, Rotterdam, The Netherlands, University of Basel, Allschwil, Switzerland, Inria Nancy Grand Est, Villers-ls-Nancy, France, ICube, Universit de Strasbourg, CNRS, Strasbourg, France, National Center for Tumor Diseases (NCT/UCC), Dresden, Germany, Ji, GP. on CVC-300-TV, in all metrics. Erasmus MC - University Medical Center Rotterdam, Rotterdam, The Netherlands, University of Basel, Allschwil, Switzerland, Inria Nancy Grand Est, Villers-ls-Nancy, France, ICube, Universit de Strasbourg, CNRS, Strasbourg, France, National Center for Tumor Diseases (NCT/UCC), Dresden, Germany, https://dl.acm.org/doi/10.1007/978-3-030-87193-2_14. Lecture Notes in Computer Science(), vol 12901. 9351, pp. https://doi.org/10.1007/978-3-030-59725-2_25, Zhang, R., Zheng, Y., Poon, C.C., Shen, D., Lau, J.Y. VPS Benchmark 6. We also conduct extensive experiments to study the effectiveness of the channel split, soft-attention, and progressive learning strategy. 6972 (2018), Ba, J.L., Kiros, J.R., Hinton, G.E. PNS-Net/README.md at main GewelsJI/PNS-Net GitHub Please refer to, If you wanna know more descriptions about our SUN-SEG dataset. We present the first comprehensive video polyp segmentation (VPS) study in the deep learning era. In: Shen, D., et al. If you have any questions about our paper, feel free to contact me. We conduct three experiments on The motivation is that a large kernel with large dilation rates may damage the integrity in the spatial-temporal representation. Contents 1. During inference, we sample T=5 frames from a polyp clip and resize them to 256448 as the input. Relevance Measuring. Our PNS-Net is based solely on a basic normalized self-attention block, dispensing with recurrence and CNNs entirely. These methods train a classifier to separate the polyps from the background. Real data for a real clinical problem. A tag already exists with the provided branch name. LNCS, vol. (eds.) : Polyp detection during colonoscopy using a regression-based convolutional neural network with a tracker. Most attention strategies aim to refine candidate features, such as first-order[fan2020pra] and second-order[wang2018non, vaswani2017attention] functions. 2252255 (2019), Krhenbhl, P., Koltun, V.: Efficient inference in fully connected CRFs with gaussian edge potentials. APT-Net: Adaptive encoding and parallel decoding transformer for Q1. We observe that #8 (PNS-Netsetting) is significantly better than #7 and #9, with R=2, in all metrics on CVC-300-TV and CVC-612-T. Springer, Cham (2020). (eds.) Re. In: Martel, A.L., et al. Existing video polyp segmentation(VPS) models typically employ convolutional neural networks (CNNs) to extract features. Experiments on multiple datasets, reporting on multiple metrics and good ablation tests are presented. Existing video polyp segmentation (VPS) models typically employ convolutional neural networks (CNNs) to extract features. https://doi.org/10.1007/978-3-030-87193-2_14, DOI: https://doi.org/10.1007/978-3-030-87193-2_14, eBook Packages: Computer ScienceComputer Science (R0). Datasets. arXiv preprint arXiv:1607.06450 (2016), Bernal, J., Snchez, F.J., Fernndez-Esparrach, G., Gil, D., Rodrguez, C., Vilario, F.: Wm-dova maps for accurate polyp highlighting in colonoscopy: validation vs. saliency maps from physicians. Further, extensive ablation studies demonstrate that the core components in our PNS-Netare all effective. Notably, based on some necessary privacy-preserving considerations from the SUN dataset, we could not directly share the download link of the video dataset with you without authorization. We have proposed a self-attention based framework, PNS-Net, (eds.) Significant efforts have been dedicated to overcoming these challenges. DOI: https://doi.org/10.1007/978-3-030-87193-2_14. In: IEEE ISM, pp. : Fully convolutional neural networks for polyp segmentation in colonoscopy. which can be found in this download link (OneDrive, 2GB). LNCS, vol. Re. The best scores are highlighted in bold. [2021/12/09] The pretraining & training code is released. We adopt the dilated convolution with a 3x3 kernel size due to the performance-efficiency trade-off. Please ensure the base. Further, the self-attention, such as the non-local network [wang2018non], incurs a high computational and memory cost, which limits the inference speed for our fast and dense prediction task. (eds.) https://doi.org/10.1007/978-3-319-24574-4_28, Tajbakhsh, N., Gurudu, S.R., Liang, J.: Automated polyp detection in colonoscopy videos using shape and context information. https://doi.org/10.1007/978-3-030-32239-7_34, CrossRef 142152Cite as, Part of the Lecture Notes in Computer Science book series (LNIP,volume 12901). Experi- Springer, Cham (2020). STM, Video Object Segmentation using Space-Time Memory Networks by Wug Oh et al. Progressively Normalized Self-Attention Network for Video Polyp on SUN-SEG-Easy (Unseen). AAAI 34, 1086910876 (2020), Guo, L., Liu, J., Zhu, X., Yao, P., Lu, S., Lu, H.: Normalized and geometry-aware self-attention network for image captioning. This is the only section that I would recommend rewriting- it is very dense and gives no help to the reader on why it makes sense and is worth a novelty tick. In: Martel, A.L., et al. Besides, we will provide some interesting resources about human colonoscopy. Thus, we test the T-2 frames by removing the first and end frames for a fair comparison. To alleviate the computational burden, we adopt an RFB-like[liu2018receptive] module to reduce the feature channel. Or you can directly download the complete VPS benchmark including prediction map of each competitor at download link: OneDrive / Baidu Drive (Password: 2t1l, Size: 5.45G). This operation can ensemble different attention regions in the network because various heads may focus on a different region in the feature map. Qualitative Comparison. As shown inTab. Over the years, developments on VPS are not moving forward with ease since large-scale fine-grained segmentation masks are still not made publicly available. Datasets & Benchmarks 2.1.1. Automatic Polyp Segmentation via Multi-scale Subtraction Network - Springer Using Guided Self-Attention with Local Information for Polyp However, they usually suffer from low accuracy due to the limited representation capability of handcrafted features in depicting heterogeneous polyps, as well as the close resemblance between polyps and hard mimics[yu2016integrating]. We re-train five cutting-edge polyp segmentation baselines (i.e., UNet[ronneberger2015u], UNet++[zhou2018unetplus], ResUNet[jha2019resunetplus], ACSNet[zhang2020adaptive], and PraNet[fan2020pra]) with the same data used by our PNS-Net, under their default settings, for fair comparison. Medical Image Computing and Computer Assisted Intervention MICCAI 2021. [Show full abstract] Self-attention Network), which can efficiently learn representations from polyp videos with real-time speed (\(\sim \)140fps) on a single RTX 2080 GPU and no post-processing . MICCAI 2021 - Accepted Papers and Reviews, Copyright 2021. Rigorous evaluation on public data provides experimental support. This could be benefitial for other similar video segmentation tasks. Main strength of this paper is to introduce self-attention mechanism to segment colonic polyp regions from colonoscopic video images. The essential connection is the gradient-induced transition, representing a soft grouping between context and texture features. For everything else, email us at [emailprotected]. processing or any operations of this database. Springer, Cham (2019). 10134, p. 101340F (2017), Fan, D.P., Cheng, M.M., Liu, Y., Li, T., Borji, A.: Structure-measure: a new way to evaluate foreground maps. For final prediction, we use the output Pt of the network followed by a sigmoid function. : Cognitive vision inspired object segmentation metric and loss function. Re. pp : Pranet: parallel reverse attention network for polyp segmentation. Similar to relevance measuring, we also compute the spatial-temporally aggregated features MTi within the constrained neighborhood during temporal aggregation. 285294. : Kvasir-SEG: a segmented polyp dataset. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. Q3. Metrics. Thus, dynamically updating the receptive field of the network is important. Progressively Normalized Self-Attention Network for Video Polyp Segmentation. Re: We are pleased to apply this fantastic training strategy to our model. 3D Graph-S2Net: Shape-Aware Self-Ensembling Network for Semi-Supervised Segmentation with Bilateral Graph Convolution. LNCS, vol. (2) the proposed method not only achieve state-of-the-art performance in segmentation measurements, e.g. Early diagnosis of colorectal cancer (CRC) plays a vital role in improving the survival rate of CRC patients. : Attention is all you need. And if you like our PNS-Net or evaluation toolbox for your personal research, please cite this paper (BibTeX). We provide an out-of-the-box evaluation toolbox for the VPS task, which is written in Python style. We provide additional anno-tation covering diverse types, i.e., attribute, object mask, boundary, scribble, and polygon. 253262. 12266, pp. Exploring the performance of PNS-Neton a larger VPS dataset will be left to our future work. PDF Video Polyp Segmentation: A Deep Learning Perspective - arXiv.org just run MyTest_finetune.py to generate the final prediction map in ./res. : Layer normalization. (eds.) Spatial-Temporal Aggregation. You switched accounts on another tab or window. To further demonstrate the generalization ability of our spatiotemporal learning framework, we extend MATNet to another relevant task: dynamic visual attention prediction (DVAP). Our PNS-Netachieves a speed of 140fps on a single RTX 2080 GPU without any post-processing (e.g., CRF[krahenbuhl2011efficient]). There are four features of our work: Figure 1: Annotation of SUN-SEG dataset. We also conduct extensive experiments to study the effectiveness of the channel split, soft-attention, and progressive learning strategy. 2 datasets, labmlai/annotated_deep_learning_paper_implementations Thanks. LNCS, vol. Effectiveness of Soft-attention. As such, the strong semantic information in high-level features might be diffused gradually during the forward pass of the network. Video Polyp Segmentation: A Deep Learning Perspective (MIR 2022) IEEE JBHI 21(1), 6575 (2016), Zhang, R., Li, G., Li, Z., Cui, S., Qian, D., Yu, Y.: Adaptive context selection for polyp segmentation. 77947803 (2018), Yang, F., Yang, H., Fu, J., Lu, H., Guo, B.: Learning texture transformer network for image super-resolution. And please inform us of your institution and the purpose of using SUN-SEG in the email. sign in 253262. Check if you have access through your login credentials or your institution to get full access on this article. Mori Laboratory, Graduate School of Informatics, Nagoya University developed this database. Experi-ments on challenging VPS datasets demonstrate that the proposedPNS-Net achieves state-of-the-art performance. Experiments on challenging VPS datasets demonstrate that the proposed PNS-Netachieves state-of-the-art performance. : Resunet++: an advanced architecture for medical image segmentation. Paper List 2.1. a single GeForce RTX 2080 GPU of 8 GB Memory. Papers With Code is a free resource with all data licensed under, U-Net: Convolutional Networks for Biomedical Image Segmentation, labmlai/annotated_deep_learning_paper_implementations, UNet++: A Nested U-Net Architecture for Medical Image Segmentation, PraNet: Parallel Reverse Attention Network for Polyp Segmentation, Video Polyp Segmentation: A Deep Learning Perspective, Progressively Normalized Self-Attention Network for Video Polyp Segmentation, See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese Networks, MATNet: Motion-Attentive Transition Network for Zero-Shot Video Object Segmentation, IEEE Transactions on Image Processing 2020, Dynamic Context-Sensitive Filtering Network for Video Salient Object Detection, Shallow Attention Network for Polyp Segmentation, Full-Duplex Strategy for Video Object Segmentation. One of the contribution introduced in this paper is the Channel Split mechanism. To fully utilize the temporal and spatial cues, we propose a simple normalized self-attention (NS) block. Please contact us for commercial use or if you are uncertain about MMM 2020. Q3. Baselines. This repository provides code for paper "Progressively Normalized Self-Attention Network for Video Polyp Segmentation" published at the MICCAI-2021 conference (arXiv Version & Springer version). Encoder. Suppose we have a colonoscopy video clip which is constituted by n frames \(X = \{x_i\}_{i=1}^n\) for training, including M frames with pixel-wise annotations, donated as L, and other \(N-M\) frames without annotations, donated as U.The goal of this task is to train the video segmentation model using L and U . Springer, Cham (2020). Are you sure you want to create this branch? 11962, pp. 4 benchmarks No intuitive explanation of the approach Motivation. 6972 (2018), Ba, J.L., Kiros, J.R., Hinton, G.E. This repository provides code for paper" Progressively Normalized Self-Attention Network for Video Polyp Segmentation " published at the MICCAI-2021 conference ( arXiv Version | ). We adopt the standard cross-entropy loss function in the learning process. The paper proposes a method for endoscopic polyp segmentation that uses an adaptive/dynamic kernel for optimizing features relevant to a polyp; the method also utilized two new attention modules ESA and LCA. LNCS, vol. Re. As long as you use the database for these purposes, you can edit or process images and annotations in this database. (2021). 10134, p. 101340F (2017), Fan, D.P., Cheng, M.M., Liu, Y., Li, T., Borji, A.: Structure-measure: a new way to evaluate foreground maps. 1032710336 (2020), Jha, D., et al. Then we concatenate a group of the spatial-temporally aggregated features MTi along the channel dimension to generate MT. https://doi.org/10.1007/978-3-030-37734-2_37, Jha, D., et al. It includes 119 colonoscopy imaging-based AI scientific research in recent 12 years. Task-Relevant Feature Replenishment for Cross-Centre Polyp Segmentation Huazhu Fu, CVC-300-TV consists of both validation set and test set, which include six videos in total. In: IEEE ISM, pp. Existing video polyp segmentation (VPS) models typically employ convolutional neural networks (CNNs) to extract features. 385400 (2018), Mamonov, A.V., Figueiredo, I.N., Figueiredo, P.N., Tsai, Y.H.R. : Pyramid constrained self-attention network for fast video salient object detection. Q2. Effectiveness of Progressive NS. In this paper, we propose the novel PNS-Net (Progressively Normalized Self . The whole project will be available at the time of MICCAI-2021. CVPR 2019. Authors: On CVC-300, where all the baseline methods perform poorly, our PNS-Netachieves remarkable performance in all metrics and outperforms all SOTA methods by a large margin (max Dice: 10%). Reinforced self-attention network: a hybrid of hard and soft attention for sequence modeling. The critical component is a normalized self-attention block to learn efficient spatio-temporal representations of polyps. Kitty K. Y. Wong, The MICCAI Society, https://doi.org/10.1007/978-3-030-87193-2_14, https://miccai2021.org/en/REVIEWER-GUIDELINES.html, Projection-wise Disentangling for Fair and Interpretable Representation Learning: Application to 3D Facial Shape Analysis, Pristine annotations-based multi-modal trained artificial intelligence solution to triage chest X-Ray for COVID19. We thank the authors for sharing the codes. We find that our PNS-Networks well under different settings, making it a promising solution to the VPS task. However, the internal covariate shift problem[guo2020normalized] exists in the feed-forward of input Qi, incurring that the layer parameters cannot dynamically adapt the next mini-batch. Authors: . However, due to their limited receptive fields, CNNs cannot fully exploit the global temporal and spatial information in successive video frames, resulting in false positive segmentation results. IEEE Transactions on Image Processing 2020. We experimentally show that our PNS-Netachieves the best performance on all existing publicly available datasets under six metrics. Some methods also focus on area-boundary constraints. International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021: Medical Image Computing and Computer Assisted Intervention MICCAI 2021 (2) Query-Dependent Rule: How does the kernel size k selected? Ge-Peng Ji, Yu-Cheng Chou, Deng-Ping Fan, Geng Chen, Huazhu Fu, Debesh Jha, Ling Shao. Qualitative and quantitative results show the effectiveness of the proposed method. One-key evaluation is written in MATLAB code (link), This work is the extension version of our conference paper (Progressively Normalized Self-Attention Network for Video Polyp Segmentation) accepted at MICCAI-2021. Experiments on challenging VPS datasets demonstrate that the proposed PNS-Net achieves state-of-the-art performance. Springer, Cham (2020). Given a polyp video clip with T frames as input (i.e., {I}Tt=1RHW3), 12266, pp. These limitations can be resolved with automatic polyp segmentation techniques, which segment polyps from colonoscopy images/videos without intervention from physicians. footnotetext: G.-P. Ji and Y.-C. Chou contributed equally. International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021: Medical Image Computing and Computer Assisted Intervention MICCAI 2021 This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 11962, pp. Indeed, the specific implementation manner of multi-head in the transformer is channel split. Re. My clinical side question is how do your treat vague boundary of a polyp. Springer, Cham (2020). G.-P. Ji and Y.-C. ChouContributed equally. IEEE JBHI 21(1), 6575 (2016), Zhang, R., Li, G., Li, Z., Cui, S., Qian, D., Yu, Y.: Adaptive context selection for polyp segmentation. In this paper, we propose the novel PNS-Net (Progressively Normalized Self-attention Network), which can efficiently learn representations from polyp videos with real-time speed (~140fps) on a single RTX 2080 GPU and no post-processing. BSCA-Net: Bit Slicing Context Attention network for polyp segmentation PDF Automatic Polyp Segmentation with Multiple Kernel Dilated Convolution Thank you for your understanding! Second, we design a simple but efficient baseline, dubbed PNS+, consisting of a global encoder, a local encoder, and normalized self-attention (NS) blocks. Please There is large consent that successful training of deep networks requires many thousand annotated training samples. Testing and Runtime. : Automated polyp detection in colon capsule endoscopy. This can be formulated as: Soft-Attention. This paper presents a novel person re-identification model, named Multi-Head Self-Attention Network (MHSA-Net), to prune unimportant information and capture key local information from person images. Simple normalized self-attention block is new idea. (eds.) 295305. 13 Jun 2020. Image-level Polyp 2.1.2. : Integrating online and offline three-dimensional deep learning for automated polyp detection in colonoscopy videos. : Endoscopic polyp segmentation using a hybrid 2D/3D CNN. et al. GewelsJI/FSNet MICCAI 2019. : Cognitive vision inspired object segmentation metric and loss function. 2. We combine the low-level feature {Xlt}Tt=1 from the spatial decoder and the spatial-temporal feature {Xrt}Tt=1 from the PNS block via a two-stage UNet-like decoder FD. 3D Semantic Mapping from Arthroscopy using Out-of-distribution Pose and Depth and In-distribution . (5) enhanced-alignment measure[21SC-Emeasure] (E), which measures pixel-level matching and image-level statistics; Recently, the self-attention network [22] has shown superior performance in computer vision tasks such as video object segmentation [10], image super- resolution [23], and others. 11 papers with code Shen et al. To tackle this issue, we propose a novel Task-relevant Feature Replenishment based Network (TRFR-Net) for cross-centre polyp segmentation via retrieving task-relevant knowledge for sufficient discrimination capability with style variations alleviated. To better understand the development of this field and to quickly push researchers in their research process, we elaborately build a Paper Reading List. We set \(H^{l}=\frac{H'}{4}\), \(W^{l}=\frac{W'}{4}\), \(C^{l}=24\), \(H^{h}=\frac{H'}{8}\), \(W^{h}=\frac{W'}{8}\), and \(C^{h}=32\). Work fast with our official CLI. arXiv Vanity renders academic papers from Please enjoy it. In: IEEE CVPR, pp. Due to the limited video training data, we try to fully utilize large-scale image data to capture more appearances of the polyp and scene. Your file of search results citations is now ready. Reviewer #1 (R1) However, accurate and real-time polyp segmentation is a challenging task due to the low boundary contrast between a polyp and its surroundings and the large shape variation of polyps[fan2020pra]. 11045, pp. PR 83, 209219 (2018), Zhao, X., Zhang, L., Lu, H.: Automatic polyp segmentation via multi-scale subtraction network. (1) maximum Dice (maxDice), which measures the similarity between two sets of data; MMM 2020. https://doi.org/10.1007/978-3-030-37734-2_37, Jha, D., et al. MICCAI 2019. The global and local encoders receive an anchor frame and multiple successive frames to extract long-term and short-term spatial-temporal representations, which are then progressively updated by two NS blocks. https://doi.org/10.1007/978-3-030-59725-2_29, Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. Distribution: It is prohibited to sell, transfer, lend, lease, resell, distribute, etc., as it is, or copy, edit, or process this database, in whole or in part. Empirically, we recommend increasing the number of NS blocks when training on larger datasets. However, accurate segmentation in the colonoscopy. In: Stoyanov, D., et al. IEEE TMI 35(2), 630644 (2015), Vaswani, A., et al. Figure 4: Quantitative comparison on two testing sub-datasets, i.e., SUN-SEG-Easy (Unseen) and SUN-SEG-Hard (Unseen). Any commercial usage should get formal permission first. SSI (2020), Fan, D.P., et al. We find that our PNS-Net works well under different settings, making it a promising solution to the VPS task. Please download or close your previous search result export first before starting a new bulk export. This improvement illustrates that too many iterations of NS blocks may cause overfitting on small datasets (#9). We use a soft-attention block to synthesize features from the group of To tackle this issue, we first introduce a long-awaited high-quality per-frame annotated VPS dataset. Springer, Cham (2015). IEEE TMI 33(7), 14881502 (2014), Murugesan, B., Sarveswaran, K., Shankaranarayana, S.M., Ram, K., Joseph, J., Sivaprakasam, M.: Psi-Net: shape and boundary aware joint multi-task deep network for medical image segmentation. 10 share In the deep learning era, we present the first comprehensive video polyp segmentation (VPS) study. weijun88/sanet Zhonget al. Springer, Cham (2020). we validate it on challenging datasets, including the test set of CVC-612 (i.e., CVC-612-T), the validation set of CVC-612 (i.e., CVC-612-V), and the test/validation set of CVC-300 (i.e., CVC-300-TV). tfzhou/MATNet https://doi.org/10.1007/978-3-319-24574-4_28, Tajbakhsh, N., Gurudu, S.R., Liang, J.: Automated polyp detection in colonoscopy videos using shape and context information. In: MICCAI (2021), Zhong, J., Wang, W., Wu, H., Wen, Z., Qin, J.: PolypSeg: an efficient context-aware network for polyp segmentation from colonoscopy videos. On the other hand, when the split number is too small, the model fails to capture multi-scale polyps moving at various speeds.
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