This is the accompanying table for Mirikharaji et al., "A Survey on Deep Learning for Skin Lesion Segmentation", 2023. A downloadable version is also available as a Google Sheets document.
| Index | Paper Title | Year | Venue | Datasets | Architectural Modules | Loss Function | Performance (Jaccard) | Cross-data Evaluation | Augmentation | Post-processing | Code Provided |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Jafari et al., Skin lesion segmentation in clinical images using deep learning | 2016 | peer-reviewed conference | DermQuest | image pyramid | - | - | ✗ | - | ✓ | ✗ |
| 2 | He et al., Skin lesion segmentation via deep RefineNet | 2017 | peer-reviewed conference | ISIC2016, ISIC2017 | residual con., skip con., image pyramid | Dice, CE, DS | 75.80% | ✗ | rotation | ✓ | ✗ |
| 3 | Bozorgtabar et al., Skin lesion segmentation using deep convolution networks guided by local unsupervised learning | 2017 | peer-reviewed journal | ISIC2016 | - | - | 80.60% | ✗ | rotation | ✗ | ✗ |
| 4 | Ramachandram and Taylor, Skin lesion segmentation using deep hypercolumn descriptors | 2017 | peer-reviewed journal | ISIC2017 | - | CE | 79.20% | ✗ | rotation, flipping, color jittering | ✗ | ✗ |
| 5 | Yu et al., Automated melanoma recognition in dermoscopy images via very deep residual networks | 2017 | peer-reviewed journal | ISIC2016 | skip con., residual con. | - | 82.90% | ✗ | rotation, translation, random noise, cropping | ✗ | ✓ |
| 6 | Bi et al., Dermoscopic image segmentation via multistage fully convolutional networks | 2017 | peer-reviewed journal | ISIC2016, PH2 | - | CE | 84.64% | ✓ | flipping, cropping | ✓ | ✗ |
| 7 | Jafari et al., Extraction of skin lesions from non-dermoscopic images for surgical excision of melanoma | 2017 | peer-reviewed journal | DermQuest | image pyramid | - | - | ✗ | - | ✓ | ✗ |
| 8 | Yuan et al., Automatic skin lesion segmentation using deep fully convolutional networks with jaccard distance | 2017 | peer-reviewed journal | ISIC2016, PH2 | - | Tanimoto | 84.7% | ✓ | flipping, rotation, scaling, shifting, contrast norm. | ✓ | ✗ |
| 9 | Ramachandram and DeVries, LesionSeg: semantic segmentation of skin lesions using deep convolutional neural network | 2017 | non peer-reviewed technical report | ISIC2017 | dilated conv. | CE | 64.20% | ✗ | rotation, flipping | ✓ | ✗ |
| 10 | Bozorgtabar et al., Investigating deep side layers for skin lesion segmentation | 2017 | peer-reviewed conference | ISIC2016 | - | CE | 82.90% | ✗ | rotations | ✓ | ✗ |