Deep Learning Based Steatosis Quantification of Liver Histopathology Images Using Unsupervised Feature Extraction
2nd International Conference on Computing and Machine Intelligence (ICMI), İstanbul, Turkey, 15 - 16 July 2022, pp.101-104, (Full Text)
- Publication Type: Conference Paper / Full Text
- Doi Number: 10.1109/icmi55296.2022.9873795
- City: İstanbul
- Country: Turkey
- Page Numbers: pp.101-104
- Keywords: steatosis quantification, deep learning, UNet, transfer learning
- Dokuz Eylül University Affiliated: No
Abstract
Steatosis quantification is an essential issue for accurate diagnosis and donor transplantation. However, manually quantification processes of steatosis by a pathologist have some difficulties because of time-consuming and tiring processes that can vary in inter and intra-experts. In recent years, deep learning studies have emerged with promising performance on steatosis quantification. On the other hand, deep learning models require a large amount of data, yet the steatosis dataset is insufficient for deep models. Thus, we propose deep learning model consisting of two steps that showed high performance even on a small number of steatosis datasets. The first step is unsupervised feature extraction with UNet. The second step is classification by using extracted features as an input for classification models. ResNet-50, EfficientNet B1 and MobileNetV2 networks are used for classification. As a result, the proposed deep models enable fully automated steatosis quantification with high AUC.