A Swin Transformer and Residualnetwork Combined Model for Breast Cancer Disease Multi-Classification Using Histopathological Images

Breast cancer has become a killer of women's health nowadays.In order to exploit the potential representational capabilities of the models more comprehensively,we propose a multi-model fusion strategy.Specifically,we combine two differently structured deep learning models,ResNet101 and Swin Transformer(SwinT),with the addition of the Convolutional Block Attention Module(CBAM)attention mechanism,which makes full use of SwinT's global context information modeling ability and ResNet101's local feature extraction ability,and additionally the cross entropy loss function is replaced by the focus loss function to solve the problem of unbalanced allocation of breast cancer data sets.The multi-classification recognition accuracies of the proposed fusion model under 40X,100X,200X and 400X BreakHis datasets are 97.50%,96.60%,96.30 and 96.10%,respectively.Compared with a single SwinT model and ResNet 101 model,the fusion model has higher accuracy and better generalization ability,which provides a more effective method for screening,diagnosis and pathological classification of female breast cancer.

The accuracy of magnetic resonance imaging and ultrasound in evaluating the size of early-stage breast neoplasms

Objective Breast cancer is the most frequently diagnosed cancer in women. Accurate evaluation of the size and extent of the tumor is crucial in selecting a suitable surgical method for patients with breast cancer. Both overestimation and underestimation have important adverse effects on patient care. This study aimed to evaluate the accuracy of breast magnetic resonance imaging(MRI) and ultrasound(US) examination for measuring the size and extent of early-stage breast neoplasms.Methods The longest diameter of breast tumors in patients with T_(1–2)N_(0–1)M_0 invasive breast cancer preparing for breast-conserving surgery(BCS) was measured preoperatively by using both MRI and US and their accuracy was compared with that of postoperative pathologic examination. If the diameter difference was within 2 mm, it was considered to be consistent with pathologic examination.Results A total of 36 patients were imaged using both MRI and US. The mean longest diameter of the tumors on MRI, US, and postoperative pathologic examination was 20.86 mm ± 4.09 mm(range: 11–27 mm), 16.14 mm ± 4.91 mm(range: 6–26 mm), and 18.36 mm ± 3.88 mm(range: 9–24 mm). US examination underestimated the size of the tumor compared to that determined using pathologic examination(t = 3.49, P < 0.01), while MRI overestimated it(t =-6.35, P < 0.01). The linear correlation coefficients between the image measurements and pathologic tumor size were r = 0.826(P < 0.01) for MRI and r = 0.645(P < 0.01) for US. The rate of consistency of MRI and US compared to that with pathologic examination was 88.89% and 80.65%, respectively, and there was no statistically significant difference between them(χ~2 = 0.80, P > 0.05).Conclusion MRI and US are both effective methods to assess the size of breast tumors, and they maintain good consistency with pathologic examination. MRI has a better correlation with pathology. However, we should be careful about the risk of inaccurate size estimation.