Brief Review — Multitask Classification and Segmentation for Cancer Diagnosis in Mammography
Multitask Classification and Segmentation for Cancer Diagnosis in Mammography,
FCN+S-Net+C-Net, by Conservatoire National des Arts et Metiers, and GE Healthcare, 2019 MIDL, Over 30 Citations (Sik-Ho Tsang @ Medium)
- A Multi-Task learning (MTL) scheme is proposed, which combines pixel-level segmentation and global image-level classification annotations for cancer diagnosis in mammography.
- Backbone: ResNet-based FCN is used as shared backbone to extract local features.
- S-Net: The segmentation network aims at classifying each pixel in the input image into a set of K pre-defined classes. S-Net first consists in adding a transfer layer of 1×1 convolution to K feature maps.
- Then, an upsampling process is performed to create the semantic segmentation.
- A weighted cross-entropy loss Lseg to address the class-imbalance issue.
- C-Net: First, local features are aggregated with a global average pooling (GAP). The second step consists in the last fully connected layer to get the final probability of cancer. The classification loss Lcls as a standard binary cross entropy function.
- The total joint loss function is:
- One setting is to sequentially train the segmentation model and finetune local features for the classification task, and the proposed one is jointly train both tasks one.
- By pretraining the model on segmentation, the classification performance is slightly improved by 1 pt to AUC=81.37%, compared to the pure classification with AUC=80.54%.
As for the proposed joint method, a significant gain is achieved both in segmentation and classification of 3.5 pts (meanDice=38.28%) and 2.5 pts (AUC=84.02%) respectively.
The proposed joint method outperforms the sequential one for both classification and segmentation, and succeeds to capture lesions with highly precise localisation capability.