TABLE 3.
Representative DL models for breast image processing and analysis.
| Studies | DL model | Imaging modality | Dataset | Image dimensions | Tasks | Model name | Supervision methods |
|---|---|---|---|---|---|---|---|
| Cai H et al.[ 13a ] | CNN | MG | Internal dataset | 2D | Classification | AlexNet | Supervised learning |
| Aly GH et al.[ 29c ] | CNN | MG | INbreast database | 2D | Detection | YOLO | Supervised learning |
| Classification | |||||||
| Al‐Antari MA et al.[ 29b ] | CNN | MG | INbreast database | 2D | Detection | YOLO | Supervised learning |
| Classification | CNN, ResNet, InceptionResNet | ||||||
| Kim H‐E et al.[ 30d ] | CNN | MG | Internal dataset | 2D | Classification | ResNet‐34 | Supervised learning |
| Fujioka T et al.[ 30c ] | CNN | US | Internal dataset | 2D | Classification | GoogLeNet | Supervised learning |
| Huang Y et al.[ 29a ] | CNN | US | Internal dataset | 2D | Detection | ROI‐CNN | Supervised learning |
| Classification | G‐CNN | ||||||
| Kumar V et al.[ 59 ] | CNN | US | Internal dataset | 2D | Segmentation | Multi U‐net | Supervised learning |
| Dalmis MU et al.[ 30b ] | CNN | MRI (DCE‐MRI, T2WI, DWI, ADC maps) | Internal dataset | 3D | Segmentation | DenseNet | Supervised learning |
| Liu W et al.[ 30a ] | CNN | MRI (DCE‐MRI, T2WI, DWI) | Internal dataset | 3D | Classification | VGG16 | Supervised learning |
| Truhn D et al.[ 60 ] | CNN | DCE‐MRI | Internal dataset | 3D | Classification | ResNet18 | Supervised learning |
| Braman N et al.[ 61 ] | CNN | DCE‐MRI | Internal dataset | 3D | Classification | Multi‐Input CNN | Supervised learning |