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.

Imaging flow cytometry (IFC) combines the imaging capabilities of microscopy with the high throughput of flow cytometry, offering a promising solution for high-precision and high-throughput cell analysis in fields such as biomedicine, green energy, and environmental monitoring. However, due to limitations in imaging framerate and real-time data processing, the real-time throughput of existing IFC systems has been restricted to approximately 1000-10,000 events per second (eps), which is insufficient for large-scale cell analysis. In this work, we demonstrate IFC with real-time throughput exceeding 1,000,000 eps by integrating optical time-stretch (OTS) imaging, microfluidic-based cell manipulation, and online image processing. Cells flowing at speeds up to 15 m/s are clearly imaged with a spatial resolution of 780 nm, and images of each individual cell are captured, stored, and analyzed. The capabilities and performance of our system are validated through the identification of malignancies in clinical colorectal samples. This work sets a new record for throughput in imaging flow cytometry, and we believe it has the potential to revolutionize cell analysis by enabling highly efficient, accurate, and intelligent measurement.