Communicator-Driven Data Preprocessing Improves Deep Transfer Learning of Histopathological Prediction of Pancreatic Ductal Adenocarcinoma.
Simple Summary: Pancreatic cancer has a dismal prognosis and its diagnosis can be challenging. Histopathological slides can be digitalized and their analysis can then be supported by computer algorithms. For this purpose, computer algorithms (neural networks) need to be trained to detect the desired...
| Published in: | Cancers Vol. 14; no. 8; pp. 1964 - 1984 |
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| Main Authors: | , , , , , , , , , , |
| Format: | equations & formulas pictorial research tables/charts Journal Article |
| Published: |
MDPI
Apr2022
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=156504664&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 156504664 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 20726694 B74B jtl: Cancers issn: 20726694 maglogo: N pubinfo: dt: Apr2022 vid: 14 iid: 8 pid: 97109 pub: MDPI artinfo: ui: 156504664 156504664 156504664 10.3390/cancers14081964 156504664 ppf: 1964 ppct: 20 formats: tig: atl: Communicator-Driven Data Preprocessing Improves Deep Transfer Learning of Histopathological Prediction of Pancreatic Ductal Adenocarcinoma. aug: au: Kronberg, Raphael M. Haeberle, Lena Pfaus, Melanie Xu, Haifeng C. Krings, Karina S. Schlensog, Martin Rau, Tilman Pandyra, Aleksandra A. Lang, Karl S. Esposito, Irene Lang, Philipp A. affil: Department of Molecular Medicine II, Medical Faculty, Heinrich-Heine-University, Universitätsstrasse 1, 40225 Düsseldorf, Germany sug: subj: Pancreatic Neoplasms Diagnosis Adenocarcinoma Carcinoma, Ductal Data Mining Deep Learning Pancreatic Neoplasms Prognosis Histological Techniques Neoplasm Metastasis Adipose Tissue Neural Networks (Computer) Early Detection of Cancer Cancer Screening Human ab: Simple Summary: Pancreatic cancer has a dismal prognosis and its diagnosis can be challenging. Histopathological slides can be digitalized and their analysis can then be supported by computer algorithms. For this purpose, computer algorithms (neural networks) need to be trained to detect the desired tissue type (e.g., pancreatic cancer). However, raw training data often contain many different tissue types. Here we show a preprocessing step using two communicators that sort unfitting tissue tiles into a new dataset class. Using the improved dataset neural networks distinguished pancreatic cancer from other tissue types on digitalized histopathological slides including lymph node metastases. Pancreatic cancer is a fatal malignancy with poor prognosis and limited treatment options. Early detection in primary and secondary locations is critical, but fraught with challenges. While digital pathology can assist with the classification of histopathological images, the training of such networks always relies on a ground truth, which is frequently compromised as tissue sections contain several types of tissue entities. Here we show that pancreatic cancer can be detected on hematoxylin and eosin (H&E) sections by convolutional neural networks using deep transfer learning. To improve the ground truth, we describe a preprocessing data clean-up process using two communicators that were generated through existing and new datasets. Specifically, the communicators moved image tiles containing adipose tissue and background to a new data class. Hence, the original dataset exhibited improved labeling and, consequently, a higher ground truth accuracy. Deep transfer learning of a ResNet18 network resulted in a five-class accuracy of about 94% on test data images. The network was validated with independent tissue sections composed of healthy pancreatic tissue, pancreatic ductal adenocarcinoma, and pancreatic cancer lymph node metastases. The screening of different models and hyperparameter fine tuning were performed to optimize the performance with the independent tissue sections. Taken together, we introduce a step of data preprocessing via communicators as a means of improving the ground truth during deep transfer learning and hyperparameter tuning to identify pancreatic ductal adenocarcinoma primary tumors and metastases in histological tissue sections. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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