Robust texture analysis of multi-modal images using Local Structure Preserving Ranklet and multi-task learning for breast tumor diagnosis

Multi-modality relation attention network for breast tumor classification

Automatic breast image classification plays an important role in breast cancer diagnosis, and multi-modality image fusion may improve classification performance. However, existing fusion methods ignore relevant multi-modality information in favor of improving the discriminative ability of single-modality features. To improve classification performance, this paper proposes a multi-modality relation attention network with consistent regularization for breast tumor classification using diffusion-weighted imaging (DWI) and apparent dispersion coefficient (ADC) images. Within the proposed network, a novel multi-modality relation attention module improves the discriminative ability of single-modality features by exploring the correlation information between two modalities. In addition, a module ensures the classification consistency of ADC and DWI modality, thus improving robustness to noise. Experimental results on our database demonstrate that the proposed method is effective for breast tumor classification, and outperforms existing multi-modality fusion methods. The AUC, accuracy, specificity, and sensitivity are 85.1%, 86.7%, 83.3%, and 88.9% respectively.

LRSCnet: Local Reference Semantic Code learning for breast tumor classification in ultrasound images

Purpose

This study proposed a novel Local Reference Semantic Code (LRSC) network for automatic breast ultrasound image classification with few labeled data.

Methods

In the proposed network, the local structure extractor is firstly developed to learn the local reference which describes common local characteristics of tumors. After that, a two-stage hierarchical encoder is developed to encode the local structures of lesion into the high-level semantic code. Based on the learned semantic code, the self-matching layer is proposed for the final classification.

Results

In the experiment, the proposed method outperformed traditional classification methods and AUC (Area Under Curve), ACC (Accuracy), Sen (Sensitivity), Spec (Specificity), PPV (Positive Predictive Values), and NPV(Negative Predictive Values) are 0.9540, 0.9776, 0.9629, 0.93, 0.9774 and 0.9090, respectively. In addition, the proposed method also improved matching speed.

Conclusions

LRSC-network is proposed for breast ultrasound images classification with few labeled data. In the proposed network, a two-stage hierarchical encoder is introduced to learn high-level semantic code. The learned code contains more effective high-level classification information and is simpler, leading to better generalization ability.

A Selective Ensemble Classification Method Combining Mammography Images with Ultrasound Images for Breast Cancer Diagnosis

Breast cancer has been one of the main diseases that threatens women's life. Early detection and diagnosis of breast cancer play an important role in reducing mortality of breast cancer. In this paper, we propose a selective ensemble method integrated with the KNN, SVM, and Naive Bayes to diagnose the breast cancer combining ultrasound images with mammography images. Our experimental results have shown that the selective classification method with an accuracy of 88.73% and sensitivity of 97.06% is efficient for breast cancer diagnosis. And indicator R presents a new way to choose the base classifier for ensemble learning.