Deep Feature Pyramid Convolutional Networks with In-Place Activated Batch Normalization for Automated Skin Lesion Boundary Segmentation

[1812.00877] Deep Feature Pyramid Convolutional Networks with In-Place Activated Batch Normalization for Automated Skin Lesion Boundary Segmentation Skip to main content Search Submit Donate Log in Computer Science > Computer Vision and Pattern Recognition arXiv:1812.00877 (cs) [Submitted on 23 Nov 2018 ( v1 ), last revised 25 Aug 2026 (this version, v2)] Title: Deep Feature Pyramid Convolutional Networks with In-Place Activated Batch Normalization for Automated Skin Lesion Boundary Segmentation Authors: Glib Kechyn View PDF HTML (experimental) Abstract: Segmentation of skin lesion boundaries in dermoscopic imaging is an important prerequisite step for computer-aided diagnosis of malignant melanoma, but remains challenging due to fuzzy margins, occluding artifacts such as hair and blood vessels, low contrast, and high inter-patient variability. This work presents a memory-efficient deep convolutional neural network framework for lesion boundary segmentation, developed for the ISIC 2018 Challenge (Task 1: Lesion Boundary Segmentation). A U-Net-style encoder-decoder architecture is adapted using Wide ResNet38 and Dual Path Network (DPN) backbones with a Feature Pyramid Network (FPN) decoder, pretrained on ImageNet. In-Place Activated Batch Normalization (InPlace-ABN) reduces memory consumption during training, allowing higher-capacity ensembling under standard GPU memory constraints, combined with five-fold cross-validation, extensive augmentation, snapshot ensembling, and test-time augmentation. The best-performing model achieves a Thresholded Jaccard score of 0.752 on the ISIC 2018 Challenge evaluation, with single-model configurations scoring between 0.700 and 0.750; InPlace-ABN reduces memory consumption by approximately 25%. To contextualize this result against current practice, a simplified single-model U-Net baseline retrained in 2026 using standard modern tooling is also reported, achieving a Dice score of 0.8443 (IoU 0.7608, Thresholded Jaccard 0.6680), which highlights the substantial contribution of ensembling and pretraining to the original result. This work does not claim state-of-the-art segmentation accuracy; rather, it documents a memory-efficient training approach that enables higher-capacity ensembling under constrained GPU resources, a practical consideration for training high-capacity segmentation models in settings with limited computational budgets. Comments: Extended version with 2026 single-model baseline comparison Subjects: Computer Vision and Pattern Recognition (cs.CV) ; Machine Learning (cs.LG); Machine Learning (stat.ML) Cite as: arXiv:1812.00877 [cs.CV] (or arXiv:1812.00877v2 [cs.CV] for this version) https://doi.org/10.48550/arXiv.1812.00877 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Glib Kechyn [ view email ] [v1] Fri, 23 Nov 2018 22:36:36 UTC (996 KB) [v2] Tue, 25 Aug 2026 07:16:28 UTC (2,334 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.CV < prev | next > new | recent | 2018-12 Change to browse by: cs cs.LG stat stat.ML References & Citations SciX Google Scholar Semantic Scholar DBLP - CS Bibliography listing | bibtex Glib Kechyn export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. 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