Pathology4 Goode et al., 2013, OpenSlide: A vendor-neutral software foundation for digital pathology. # 세줄 요약 # Although widely touted as a replacement for glass slides and microscopes in pathology, digital slides present major challenges in data storage, transmission, processing, and interoperability. In this paper, we present the design and implementation of OpenSlide, a vendor-neutral C library (easily extensible for various programming languages) for reading and manipulating digital slides o.. 2022. 2. 13. Wang et al., 2019, Pathology Image Analysis Using Segmentation Deep Learning Algorithms. # 세줄 요약 # With the rapid development of image scanning techniques and visualization software, whole slide imaging (WSI) is becoming a routine diagnostic method. Deep learning-based pathology image segmentation has become an important tool in WSI analysis because that algorithms such as fully convolutional networks stand out for their accuracy, computational efficiency, and generalizability. In t.. 2021. 12. 20. Campanella et al., 2019, Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. # 세줄 요약 # The development of decision support systems for pathology and their deployment in clinical practice have been hindered by the need for large manually annotated datasets. We present a multiple instance learning-based deep learning system that uses only reported diagnoses as labels for training. Tests on prostate cancer, basal cell carcinoma and breast cancer metastases to axillary lymph.. 2021. 10. 4. Kanavati et al., 2021, A deep learning model for the classification of indeterminate lung carcinoma in biopsy whole slide images # 세줄 요약 # The differentiation between major histological types of lung cancer, such as adenocarcinoma (ADC), squamous cell carcinoma (SCC), and small-cell lung cancer (SCLS) is of crucial importance for determining optimum cancer treatment. Hematoxylin and Eosin (H&E)-stained slides of small transbronchial lung biopsy (TBLB) are one of the primary sources for making a diagnosis, but if this diag.. 2021. 7. 18. 이전 1 다음
