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Lesion Attributes Segmentation for Melanoma Detection with Deep Learning

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-319-24574-4_28openalex-snapshot

U-Net: Convolutional Networks for Biomedical Image Segmentation

inferredcites →https://doi.org/10.1016/j.neucom.2016.12.038openalex-snapshot

A survey of deep neural network architectures and their applications

inferredcites →https://doi.org/10.1038/sdata.2018.161openalex-snapshot

The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions

inferredcites →https://doi.org/10.1080/24699322.2017.1389405openalex-snapshot

Melanoma segmentation based on deep learning

inferredcites →https://doi.org/10.1093/annonc/mdy166openalex-snapshot

Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists

inferredcites →https://doi.org/10.1093/annonc/mdy520openalex-snapshot

Reply to the letter to the Editor "Reply to 'Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists' by H. A. Haenssle et al. " by L. Oakden-Rayner

inferredcites →https://doi.org/10.1109/cvpr.2006.153openalex-snapshot

Incremental learning of object detectors using a visual shape alphabet

inferredcites →https://doi.org/10.1109/cvpr.2015.7298965openalex-snapshot

Fully convolutional networks for semantic segmentation

inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot

Deep Residual Learning for Image Recognition

inferredcites →https://doi.org/10.1109/cvpr.2017.243openalex-snapshot

Densely Connected Convolutional Networks

inferredcites →https://doi.org/10.1109/cvprw.2018.00042openalex-snapshot

TernausNetV2: Fully Convolutional Network for Instance Segmentation

inferredcites →https://doi.org/10.1109/icmla.2018.00100openalex-snapshot

Automatic Instrument Segmentation in Robot-Assisted Surgery using Deep Learning

inferredcites →https://doi.org/10.1109/isbi.2018.8363547openalex-snapshot

Skin lesion analysis toward melanoma detection: A challenge at the 2017 International symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC)

inferredcites →https://doi.org/10.1371/journal.pone.0193321openalex-snapshot

Acral melanoma detection using a convolutional neural network for dermoscopy images

inferredcites →https://doi.org/10.1609/aaai.v31i1.11231openalex-snapshot

Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning

inferredcites →https://doi.org/10.48550/arxiv.1409.1556openalex-snapshot

Very Deep Convolutional Networks for Large-Scale Image Recognition

inferredcites →https://doi.org/10.48550/arxiv.1601.07843openalex-snapshot

An Overview of Melanoma Detection in Dermoscopy Images Using Image Processing and Machine Learning

inferredcites →https://doi.org/10.48550/arxiv.1806.00844openalex-snapshot

TernausNetV2: Fully Convolutional Network for Instance Segmentation

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/978-3-030-63007-2_41openalex-snapshot

Macroscopic Skin Lesion Segmentation Using GrabCut

inferred← citeshttps://doi.org/10.1109/ehb47216.2019.8970033openalex-snapshot

Automatic Detection of Melanoma with Yolo Deep Convolutional Neural Networks

inferred← citeshttps://doi.org/10.25073/jaec.202044.306openalex-snapshot

Implementation of Automatic Skin Lesions Diagnosis-A Deep Learning Ensembling Approach

inferred← citeshttps://doi.org/10.3390/diagnostics13182924openalex-snapshot

Dermo-Seg: ResNet-UNet Architecture and Hybrid Loss Function for Detection of Differential Patterns to Diagnose Pigmented Skin Lesions

inferred← citeshttps://doi.org/10.48550/arxiv.1809.10243openalex-snapshot

Segmentation of Skin Lesions and their Attributes Using Multi-Scale Convolutional Neural Networks and Domain Specific Augmentations

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