{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/doubleu-net-a-deep-convolutional-neural","title":"DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation","arxiv_id":"2006.04868","date":"2020-06-08","proceeding":null,"authors":["Debesh Jha","Michael A. Riegler","Dag Johansen","Pål Halvorsen","Håvard D. Johansen"],"abstract":"Semantic image segmentation is the process of labeling each pixel of an image with its corresponding class. An encoder-decoder based approach, like U-Net and its variants, is a popular strategy for solving medical image segmentation tasks. To improve the performance of U-Net on various segmentation tasks, we propose a novel architecture called DoubleU-Net, which is a combination of two U-Net architectures stacked on top of each other. The first U-Net uses a pre-trained VGG-19 as the encoder, which has already learned features from ImageNet and can be transferred to another task easily. To capture more semantic information efficiently, we added another U-Net at the bottom. We also adopt Atrous Spatial Pyramid Pooling (ASPP) to capture contextual information within the network. We have evaluated DoubleU-Net using four medical segmentation datasets, covering various imaging modalities such as colonoscopy, dermoscopy, and microscopy. Experiments on the MICCAI 2015 segmentation challenge, the CVC-ClinicDB, the 2018 Data Science Bowl challenge, and the Lesion boundary segmentation datasets demonstrate that the DoubleU-Net outperforms U-Net and the baseline models. Moreover, DoubleU-Net produces more accurate segmentation masks, especially in the case of the CVC-ClinicDB and MICCAI 2015 segmentation challenge datasets, which have challenging images such as smaller and flat polyps. These results show the improvement over the existing U-Net model. The encouraging results, produced on various medical image segmentation datasets, show that DoubleU-Net can be used as a strong baseline for both medical image segmentation and cross-dataset evaluation testing to measure the generalizability of Deep Learning (DL) models.","url_abs":"https://arxiv.org/abs/2006.04868v2","url_pdf":"https://arxiv.org/pdf/2006.04868v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"doubleu-net-a-deep-convolutional-neural","repo_url":"https://github.com/DebeshJha/2020-CBMS-DoubleU-Net","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"doubleu-net-a-deep-convolutional-neural","repo_url":"https://github.com/Janetteeeeeeee/double_unet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"doubleu-net-a-deep-convolutional-neural","repo_url":"https://github.com/harsh-jadhav/SIIM-Pneumothorax-Case-Study2","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"doubleu-net-a-deep-convolutional-neural","repo_url":"https://github.com/mehul-k5/Double-Unet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"cell-segmentation","task_name":"Cell Segmentation"},{"task_slug":"colorectal-polyps-characterization","task_name":"Colorectal Polyps Characterization"},{"task_slug":"image-segmentation","task_name":"Image Segmentation"},{"task_slug":"lesion-segmentation","task_name":"Lesion Segmentation"},{"task_slug":"medical-image-segmentation","task_name":"Medical Image Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"skin-cancer-segmentation","task_name":"Skin Cancer Segmentation"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[{"method_slug":"concatenated-skip-connection","method_name":"Concatenated Skip Connection"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"spatial-pyramid-pooling","method_name":"Spatial Pyramid Pooling"},{"method_slug":"u-net","method_name":"U-Net"},{"method_slug":"vgg-19","method_name":"VGG-19"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/lesion-segmentation-on-isic-2018","task":"Lesion Segmentation","dataset":"ISIC 2018","model":"DoubleU-Net","rank_in_archive_order":9,"of":17,"metrics":{"mean Dice":"0.8962"},"uses_additional_data":false},{"leaderboard":"/sota/medical-image-segmentation-on-2015-miccai","task":"Medical Image Segmentation","dataset":"2015 MICCAI Polyp Detection","model":"DoubleUNet","rank_in_archive_order":1,"of":1,"metrics":{"Dice":"0.7649"},"uses_additional_data":false},{"leaderboard":"/sota/medical-image-segmentation-on-2018-data","task":"Medical Image Segmentation","dataset":"2018 Data Science Bowl","model":"DoubleUNet","rank_in_archive_order":8,"of":10,"metrics":{"Dice":"0.9133","Precision":"0.9596","Recall":"0.6407","mIoU":"0.8407"},"uses_additional_data":false},{"leaderboard":"/sota/medical-image-segmentation-on-cvc-clinicdb","task":"Medical Image Segmentation","dataset":"CVC-ClinicDB","model":"DoubleUNet","rank_in_archive_order":34,"of":48,"metrics":{"mean Dice":"0.9239"},"uses_additional_data":false},{"leaderboard":"/sota/medical-image-segmentation-on-kvasir","task":"Medical Image Segmentation","dataset":"Kvasir-Instrument","model":"DoubleUNet","rank_in_archive_order":3,"of":3,"metrics":{"DSC":"0.9038"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-segmentation-on-kvasir-instrument","task":"Semantic Segmentation","dataset":"Kvasir-Instrument","model":"DoubleUNet","rank_in_archive_order":1,"of":2,"metrics":{"DSC":"0.9038","mIoU":"0.8430"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2006.04868","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.04868"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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