With the development of artificial intelligence technology and computer hardware functions, deep learning algorithms have become a powerful auxiliary tool for medical image analysis. This study was an attempt to use statistical methods to analyze studies related to the detection, segmentation, and classification of breast cancer in pathological images. After an analysis of 107 articles on the application of deep learning to pathological images of breast cancer, this study is divided into three directions based on the types of results they report: detection, segmentation, and classification. We introduced and analyzed models that performed well in these three directions and summarized the related work from recent years. Based on the results obtained, the significant ability of deep learning in the application of breast cancer pathological images can be recognized. Furthermore, in the classification and detection of pathological images of breast cancer, the accuracy of deep learning algorithms has surpassed that of pathologists in certain circumstances. Our study provides a comprehensive review of the development of breast cancer pathological imaging-related research and provides reliable recommendations for the structure of deep learning network models in different application scenarios.

Objective . To propose a new dynamic multiparametric magnetic resonance imaging (mpMRI) radiomics method for the detection of prostate cancer (PCa), and establish a combined model using dynamic and static radiomics features based on this method. Approach . A total of 166 patients (82 PCa patients and 84 non-PCa patients) were enrolled in the study, and 31 872 mpMRI images were performed in a radiomics workflow. The whole prostate segmentation and traditional static radiomics features extraction were performed on intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI, b value of 10, 50, 100, 150, 200, 400, 600, 800, 1000, 1500 s mm ⁻² respectively), apparent diffusion coefficient (ADC), and T2-weighted imaging (T2WI) sequences respectively. Through the building of each b -value DWI model and the analysis of the static key radiomics features, three types of dynamic features called standard discrete (SD), parameter ( P ) and relative change rate (RCR) were constructed. And the b-value parameters used to construct dynamic features were divided into three groups (‘Df_’, ‘Db_’ and ‘Da_’): the front part (10–200 s mm ⁻² ), the back part (400–1500 s mm ⁻² ), and all (10–1500 s mm ⁻² ) of the b -values set, respectively. Afterwards, the dynamic mpMRI model and combined model construction were constructed, and the PCa discrimination performance of each model was evaluated. Main results. The models based on dynamic features showed good potential for PCa identification. Where, the results of Db_SD, Da_P and Db_P models were encouraging (test cohort AUCs: 90.78%, 87.60%, 86.3%), which was better than the commonly used ADC model (AUC of ADC was 75.48%). Among the combined models, the models using static features of T2WI and dynamic features performed the best. The AUC of Db_SD + T2WI, Db_P + T2WI and Db_RCR + T2WI model was 92.90%, 91.29% and 81.46%. Significance. The dynamic-static combination model based on dynamic mpMRI radiomics method has a good effect on the identification of PCa. This method has broad application prospects in PCa individual diagnosis management.

Aims: Deep-learning methods for scoring biomarkers are an active research topic. However, the superior performance of many studies relies on large datasets collected from clinical samples. In addition, there are fewer studies on immunohistochemical marker assessment for dermatological diseases. Accordingly, we developed a method for scoring CD30 based on convolutional neural networks for a few primary cutaneous CD30 ⁺ lymphoproliferative disorders and used this method to evaluate other biomarkers. Methods: A multipatch spatial attention mechanism and conditional random field algorithm were used to fully fuse tumour tissue characteristics on immunohistochemical slides and alleviate the few sample feature deficits. We trained and tested 28 CD30 ⁺ immunohistochemical whole slide images (WSIs), evaluated them with a performance index, and compared them with the diagnoses of senior dermatologists. Finally, the model’s performance was further demonstrated on the publicly available Yale HER2 cohort. Results: Compared with the diagnoses by senior dermatologists, this method can better locate the tumour area and reduce the misdiagnosis rate. The prediction of CD3 and Ki-67 validated the model’s ability to identify other biomarkers. Conclusions: In this study, using a few immunohistochemical WSIs, our model can accurately identify CD30, CD3 and Ki-67 markers. In addition, the model could be applied to additional tumour identification tasks to aid pathologists in diagnosis and benefit clinical evaluation.

Stereo vision can be achieved with a single camera aided by a flat-face prism. However, large optical distortions may be caused by irregular refraction. To mitigate this deficiency and minimize distortion, a new freeform prism design is proposed to replace the flat-face prism. All refracting surfaces of the prism are designed for freeform features, which can process object points at a large depth of field. Differential equations are derived to express the freeform surface, and the Runge-Kutta method is used to numerically solve the differential equations.

In the field of medicine, quickly and accurately determining whether the patient is malignant or benign is the key to treatment. In this paper, K-Nearest Neighbor, Linear Discriminant Analysis, Logistic Regression were applied to predict the classification of thyroid,Her-2,PR,ER,Ki67,metastasis and lymph nodes in breast cancer, in order to recognize the benign and malignant breast tumors and achieve the purpose of aided diagnosis of breast cancer. The results showed that the highest classification accuracy of LDA was 88.56%, while the classification effect of KNN and Logistic Regression were better than that of LDA, the best accuracy reached 96.30%.