Title Glomerulus classification and detection based on convolutional neural networks /
Authors Gallego, Jaime ; Pedraza, Anibal ; Lopez, Samuel ; Steiner, Georg ; Gonzalez, Lucia ; Laurinavičius, Arvydas ; Bueno, Gloria
DOI 10.3390/jimaging4010020
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Is Part of Journal of imaging.. Basel : MDPI AG. 2018, Vol. 4, iss. 1, art. no. UNSP 20, [19 p.].. ISSN 2313-433X
Keywords [eng] Glomerulus classification ; Glomerulus detection ; digital pathology ; Convolutional Neural Networks
Abstract [eng] Glomerulus classification and detection in kidney tissue segments are key processes in nephropathology used for the correct diagnosis of the diseases. In this paper, we deal with the challenge of automating Glomerulus classification and detection from digitized kidney slide segments using a deep learning framework. The proposed method applies Convolutional Neural Networks (CNNs) between two classes: Glomerulus and Non-Glomerulus, to detect the image segments belonging to Glomerulus regions. We configure the CNN with the public pre-trained AlexNet model and adapt it to our system by learning from Glomerulus and Non-Glomerulus regions extracted from training slides. Once the model is trained, labeling is performed by applying the CNN classification to the image blocks under analysis. The results of the method indicate that this technique is suitable for correct Glomerulus detection in Whole Slide Images (WSI), showing robustness while reducing false positive and false negative detections.
Published Basel : MDPI AG
Type Journal article
Language English
Publication date 2018