Development and validation of a deep learning system for ascites cytopathology interpretation
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ORIGINAL ARTICLE
Development and validation of a deep learning system for ascites cytopathology interpretation Feng Su1 · Yu Sun2 · Yajie Hu2 · Peijiang Yuan3 · Xinyu Wang2 · Qian Wang2 · Jianmin Li4 · Jia‑Fu Ji5 Received: 27 February 2020 / Accepted: 25 May 2020 © The International Gastric Cancer Association and The Japanese Gastric Cancer Association 2020
Abstract Background Early diagnosis of Peritoneal metastasis (PM) is clinically significant regarding optimal treatment selection and avoidance of unnecessary surgical procedures. Cytopathology plays an important role in early screening of PM. We aimed to develop a deep learning (DL) system to achieve intelligent cytopathology interpretation, especially in ascites cytopathology. Methods The original ascites cytopathology image dataset consists of 139 patients’ original hematoxylin–eosin (HE) and Papanicolaou (PAP) Staining images. DL system was developed using transfer learning (TL) to achieve cell detection and classification. Pre-trained alexnet, vgg16, goolenet, resnet18 and resnet50 models were studied. Cell detection dataset consists of 176 cropped images with 6573 annotated cell bounding boxes. Cell classification data set consists of 487 cropped images with 18,558 and 6089 annotated malignant and benign cells in total, respectively. Results We established a novel ascites cytopathology image dataset and achieved automatically cell detection and classification. DetectionNet based on Faster R-CNN using pre-trained resnet18 achieved cell detection with 87.22% of cells’ Intersection of Union (IoU) bigger than the threshold of 0.5. The mean average precision (mAP) was 0.8316. The ClassificationNet based on resnet50 achieved the greatest performance in cell classification with AUC = 0.8851, Precision = 96.80%, FNR = 4.73%. The DL system integrating the separately trained DetectionNet and Classificationnet showed great performance in the cytopathology image interpretation. Conclusions We demonstrate that the integration of DL can improve the efficiency of healthcare. The DL system we developed using TL techniques achieved accurate cytopathology interpretation, and had great potential to be integrated into clinician workflow. Keywords Ascites cytopathology · Deep learning · Transfer learning · CNN · Faster R-CNN
Introduction Deep learning (DL) is a landmark methodology in artificial intelligence (AI) driven by big data, high computing power, and deep network models, which has achieved
state-of-the-art performance in many challenging tasks, such as image classification, natural language processing, audio processing, and playing strategy games [1–5]. DL is capable of extracting features from sparse and noised medical data, and have obtained many excellent achievements in
* Jianmin Li [email protected]
3
School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China
* Jia‑Fu Ji [email protected]
4
Institute for Artificial Intelligence, the State Key Laboratory of Intelligence Technology and Systems, B
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