Breast cancer is one of the most common types of cancer and is the leading cause of cancer-related death. Diagnosis of breast cancer is based on the evaluation of pathology slides. In the era of digital pathology, these slides can be converted into digital whole slide images (WSIs) for further analysis. However, due to their sheer size, digital WSIs diagnoses are time consuming and challenging. In this study, we present a lightweight architecture that consists of a bilinear structure and MobileNet-V3 network, bilinear MobileNet-V3 (BM-Net), to analyze breast cancer WSIs. We utilized the WSI dataset from the ICIAR2018 Grand Challenge on Breast Cancer Histology Images (BACH) competition, which contains four classes: normal, benign, in situ carcinoma, and invasive carcinoma. We adopted data augmentation techniques to increase diversity and utilized focal loss to remove class imbalance. We achieved high performance, with 0.88 accuracy in patch classification and an average 0.71 score, which surpassed state-of-the-art models. Our BM-Net shows great potential in detecting cancer in WSIs and is a promising clinical tool.

Matching whole slide histopathology images to provide comprehensive information on homologous tissues is beneficial for cancer diagnosis. However, the challenge arises with the Giga-pixel whole slide images (WSIs) when aiming for high-accuracy matching. Learning-based methods are difficult to generalize well with large-size WSIs, necessitating the integration of traditional matching methods to enhance accuracy as the size increases. In this paper, we propose a multi-size guiding matching method applicable high-accuracy requirements. Specifically, we design learning multiscale texture to train deep descriptors, called TDescNet, that trains 64 $\times 64\times$ 256 and 256 $\times 256\times$ 128 size convolution layer as C64 and C256 descriptors to overcome staining variation and low visibility challenges. Furthermore, we develop the 3D-ring descriptor using sparse keypoints to support the description of large-size WSIs. Finally, we employ C64, C256, and 3D-ring descriptors to progressively guide refined local matching, utilizing geometric consistency to identify correct matching results. Experiments show that when matching WSIs of size 4096×4096 pixels, our average matching error is 123.48 $\upmum$ and the success rate is 93.02 $\%$ in 43 cases. Notably, our method achieves an average improvement of 65.52 $\upmum$ in matching accuracy compared to recent state-of-the-art methods, with enhancements ranging from 36.27 $\upmum$ to 131.66 $\upmum$ . Therefore, we achieve high-fidelity whole-slice image matching, and overcome staining variation and low visibility challenges, enabling assistance in comprehensive cancer diagnosis through matched WSIs.

Nuclei segmentation is a fundamental but challenging task in computational pathology due to diverse morphologies, blurred boundaries, and staining variations. Despite remarkable progress, existing models often suffer from structural instability under morphological and staining variations. We attribute this instability to disrupted frequency-spatial consistency and address it through FreqPath-Net, which enforces frequency-spatial consistency for robust nuclei segmentation. By operating directly in the frequency domain, FreqPath-Net achieves morphology-invariant and stain-robust feature representations. The Spectral Wavelet Attention Module (SWAM) adaptively enhances high-frequency boundary cues while maintaining low-frequency consistency, addressing boundary blurring and detail loss. Furthermore, the Orthogonal Direction-Constrained Frequency Module (ODFM) captures global spectral patterns and enforces directional consistency, effectively preserving boundary orientation and structural integrity by leveraging frequency-spatial consistency. Extensive experiments on twelve nuclei segmentation benchmarks show that FreqPath-Net consistently outperforms state-of-the-art methods. On the multi-organ Pan-Nuke dataset, FreqPath-Net achieves an mIoU of 85.32%, outperforming the second-best method by 2.93%. Code: https://github.com/huangjin520/FreqPath-Net.