Neurological Disorders - BJSTR Journal

Abstract

#Medical databases are fundamental for developing new techniques for early detection of #neoplastic cells. They are however difficult to obtain, since the labelling of the images is often operator dependent, requires specialized skills and the written informed consent of the patient. The variability of structures in biological tissue poses a challenge to both manual and automated analysis of histopathology slides. Although some authors showed moderate to good agreement among expert pathologists, and satisfactory results on their #intra-observer reliability, other studies found that even experienced pathologists frequently disagree on tissue classification, which may lead to the conclusion that solely using expert scoring as gold standard for #histopathological assessment could be insufficient. Hence, there is a growing demand for robust computational methods in order to increase reproducibility of diagnoses. In this note we present a database containing images of preneoplastic and neoplastic colorectal tissues and in a forthcoming paper we will describe our proposed DL algorithm to classify them into the following categories: normal mucosa, early #preneoplastic lesions, adenomas, cancer. #Colorectal cancer ranks among the three most common cancers in terms of both cancer incidence and cancer-related deaths in Western industrialized countries [1]. Every year in the world nearly 1.3 million new cases of CRC are reported and nearly 700.000 patients die [2]. Lifetime risk of colorectal cancer may reach 6% of the population living in developed countries. CRC is second in incidence in Europe only to lung cancer, and it causes around 204.000 deaths every year [3].

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Medical Database for #DetectingNeoplasticLesions in Human Colorectal Cancer with Deep Learning by Rita Fioresi in BJSTR