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- W4285213308 abstract "Dangi, Naman Deulkar, KhushaliIn medical imaging, digital pathology is a rapidly growing field, where glass slides containing tissue specimens are digitized using whole-slide scanners at very high resolutions. Virtual microscopy, also known as whole-slide imaging, aids digital pathology in the analysis, assessment, and diagnosis of tissue slides. Lymph node metastases occur in most cancer types like breast, colon, prostate, etc. Metastatic involvement of lymph nodes is a very important variable in the prognosis of breast cancer, where the diagnostic procedure for the pathologists is tedious, prone to misinterpretation, and requires large amounts of reading time from pathologists. Automated disease detection has been a long-standing challenge for computer-aided diagnostic systems; however, within the past few years, the field has been moving toward grand goals with strong potential diagnostic impact: fully automated analysis of whole-slide images to detect or grade cancer, to predict prognosis or identify metastases. In this paper, we focus on the detection of micro- and macro-metastases in hematoxylin and eosin-stained whole-slide images of lymph node sections with an aim to improve the detection of cancer metastasis, potentially reducing the workload of pathologists by a great amount while at the same time, reducing the subjectivity in diagnosis. This paper demonstrates performance analysis of different deep neural architectures deployed for automated metastases detection in whole slide images of lymph node sections and draws analogies based on the recorded results." @default.
- W4285213308 created "2022-07-14" @default.
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- W4285213308 date "2022-01-01" @default.
- W4285213308 modified "2023-09-30" @default.
- W4285213308 title "Performance Analysis of Different Deep Neural Architectures for Automated Metastases Detection of Lymph Node Sections in Hematoxylin and Eosin-Stained Whole-Slide images" @default.
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- W4285213308 doi "https://doi.org/10.1007/978-981-16-9113-3_62" @default.
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