Cancer detection for small-size and ambiguous tumors based on semantic FPN and transformer

Abstract

Early detection of tumors has great significance for formative detection and determination of treatment plans. However, cancer detection remains a challenging task due to the interference of diseased tissue, the diversity of mass scales, and the ambiguity of tumor boundaries. It is difficult to extract the features of small-sized tumors and tumor boundaries, so semantic information of high-level feature maps is needed to enrich the regional features and local attention features of tumors. To solve the problems of small tumor objects and lack of contextual features, this paper proposes a novel Semantic Pyramid Network with a Transformer Self-attention, named SPN-TS, for tumor detection. Specifically, the paper first designs a new Feature Pyramid Network in the feature extraction stage. It changes the traditional cross-layer connection scheme and focuses on enriching the features of small-sized tumor regions. Then, we introduce the transformer attention mechanism into the framework to learn the local feature of tumor boundaries. Extensive experimental evaluations were performed on the publicly available CBIS-DDSM dataset, which is a Curated Breast Imaging Subset of the Digital Database for Screening Mammography. The proposed method achieved better performance in these models, achieving 93.26% sensitivity, 95.26% specificity, 96.78% accuracy, and 87.27% Matthews Correlation Coefficient (MCC) value, respectively. The method can achieve the best detection performance by effectively solving the difficulties of small objects and boundaries ambiguity. The algorithm can further promote the detection of other diseases in the future, and also provide algorithmic references for the general object detection field.

Breast Tumour Segmentation Using Advanced UNet with Saliency, Channel, and Spatial Attention Models

“…In this process, the two attention weights 𝛼 and 𝛽 are obtained and a final feature map is generated. The complete process is expressed as: Comparative analysis: The obtained performance is compared with the existing deep learning methods such as faster RCNN [26], YOLOv3 [27], FPN [28], Mask RCNN [29], SSD [30], Cascade RCNN [31] and SPN-TS [32]. Below given table shows the comparative analysis for CBIS-DDSM dataset.…”