Generalization of prostate cancer classification for multiple sites using deep learning

Arvidsson, Ida; Overgaard, Niels Christian; Marginean, Felicia-Elena; Krzyzanowska, Agnieszka; Bjartell, Anders; Åström, Kalle; Heyden, Anders

doi:10.1109/isbi.2018.8363552

Cited by 28 publications

(17 citation statements)

References 5 publications

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“…Another way to account for stain variability is to include stain differences in the training stage—so called stain or color augmentation. Tellez et al performed stain augmentation directly on H&E channels for whole slide mitosis detection in Breast Histology, and Arvidsson et al implemented color augmentation in the HSV space to generalize CNNs for prostate cancer classification for multiple imaging sites. Bentaieb et al performed stain normalization by style transfer across datasets using a GAN coupled to a CNN for end‐to‐end histopathology image classification and tissue segmentation.…”

Section: Deep Learning For Image Cytometrymentioning

confidence: 99%

Deep Learning in Image Cytometry: A Review

et al. 2018

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Artificial intelligence, deep convolutional neural networks, and deep learning are all niche terms that are increasingly appearing in scientific presentations as well as in the general media. In this review, we focus on deep learning and how it is applied to microscopy image data of cells and tissue samples. Starting with an analogy to neuroscience, we aim to give the reader an overview of the key concepts of neural networks, and an understanding of how deep learning differs from more classical approaches for extracting information from image data. We aim to increase the understanding of these methods, while highlighting considerations regarding input data requirements, computational resources, challenges, and limitations. We do not provide a full manual for applying these methods to your own data, but rather review previously published articles on deep learning in image cytometry, and guide the readers toward further reading on specific networks and methods, including new methods not yet applied to cytometry data. © 2018 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

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Section: Deep Learning For Image Cytometrymentioning

confidence: 99%

Deep Learning in Image Cytometry: A Review

et al. 2018

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“…In last years, we observe a growing interest in the application of DL systems to support prostate cancer evaluation. In literature several related studies on prostate cancer were published [10][11][12][13][14] . Two main tasks can be distinguished: (a) cancer detection and segmentation, and (b) Gleason grading.…”

Section: Related Workmentioning

confidence: 99%

“…A tool to support pathologists' work should be robust on this type of variances. Arvidsson et al 12 proposed an auto-encoder application to prostate cancer detection. They achieved good results, with accuracy 88% on an independent data set of 39 whole slide images (WSIs).…”

Section: Related Workmentioning

confidence: 99%

Impact of rescanning and normalization on convolutional neural network performance in multi-center, whole-slide classification of prostate cancer

Świderska-Chadaj

Bel

Blanchet

et al. 2020

Sci Rep

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Algorithms can improve the objectivity and efficiency of histopathologic slide analysis. In this paper, we investigated the impact of scanning systems (scanners) and cycle-GAn-based normalization on algorithm performance, by comparing different deep learning models to automatically detect prostate cancer in whole-slide images. Specifically, we compare U-Net, DenseNet and EfficientNet. Models were developed on a multi-center cohort with 582 WSIs and subsequently evaluated on two independent test sets including 85 and 50 WSIs, respectively, to show the robustness of the proposed method to differing staining protocols and scanner types. We also investigated the application of normalization as a pre-processing step by two techniques, the whole-slide image color standardizer (WSICS) algorithm, and a cycle-GAN based method. For the two independent datasets we obtained an AUC of 0.92 and 0.83 respectively. After rescanning the AUC improves to 0.91/0.88 and after style normalization to 0.98/0.97. In the future our algorithm could be used to automatically pre-screen prostate biopsies to alleviate the workload of pathologists. Prostate cancer is the most common cancer in men and the third most common tumor type worldwide 1,2. In 2018, 1.3 million new cases have been diagnosed (7.1% of all diagnosed cancers), and 28% of these patients died as a result of the disease 1. Prostate cancer is typically diagnosed through ultrasound-guided biopsy after initial suspicion has arisen through, for example, a prostate specific antigen (PSA) blood test. During the prostate biopsy procedure, 6-12 core samples are taken from a patient 3 resulting worldwide in more than 15 million specimens annually, which is expected to increase further with the aging of the population. All these specimens have to be evaluated by pathologists. However, in many countries there is a lack of pathologists which is only expected to increase in the years to come. Automating (part of) the evaluation of prostate biopsies might help mitigate the lack of clinical pathology. The histopathological analysis could be streamlined significantly if these negative slides (i.e. slides without pathology) could automatically be excluded without expelling any slides containing cancer. Significant progress has been made in this respect, revealing the huge potential of deep learning (DL) methods 4-6. In histopathology, deep learning based algorithms have been used to solve a variety of tasks, such as mitotic Figure detection 7 , lung adenocarcinoma segmentation 4 , glomeruli detection 8 or tissue analysis in colorectal cancer 9. However, histological slides from different institutions show heterogeneous appearance as a result of the different preparation and staining procedures (different colors, intensity, saturation) (Fig. 1). As a result, there is a high probability that a model trained on data from one medical center may not be applicable to slides from another center. The key challenge is to develop a system robust to a variety of biological, staining or scanning settings.

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“…CNN performed exceptionally well in the detection of lung cancer. In [16], Chen et al proposed a deep cascade network for mitosis detection in breast histology slides. They first trained a fully connected network model to extract mitosis candidates from the whole histology slides and then finetuned a CaffeNet model for the classification of mitosis.…”

Section: Literature Reviewmentioning

confidence: 99%

Tissue Level Based Deep Learning Framework for Early Detection of Dysplasia in Oral Squamous Epithelium

Gupta¹,

Kaur²,

Manhas³

2019

J Multimed Inf Syst

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Deep learning is emerging as one of the best tool in processing data related to medical imaging. In our research work, we have proposed a deep learning based framework CNN (Convolutional Neural Network) for the classification of dysplastic tissue images. The CNN has classified the given images into 4 different classes namely normal tissue, mild dysplastic tissue, moderate dysplastic tissue and severe dysplastic tissue. The dataset under taken for the study consists of 672 tissue images of epithelial squamous layer of oral cavity captured out of the biopsy samples of 52 patients. After applying the data pre-processing and augmentation on the given dataset, 2688 images were created. Further, these 2688 images were classified into 4 categories with the help of expert Oral Pathologist. The classified data was supplied to the convolutional neural network for training and testing of the proposed framework. It has been observed that training data shows 91.65% accuracy whereas the testing data achieves 89.3% accuracy. The results produced by our proposed framework are also tested and validated by comparing the manual results produced by the medical experts working in this area.

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Generalization of prostate cancer classification for multiple sites using deep learning

Cited by 28 publications

References 5 publications

Deep Learning in Image Cytometry: A Review

Deep Learning in Image Cytometry: A Review

Impact of rescanning and normalization on convolutional neural network performance in multi-center, whole-slide classification of prostate cancer

Tissue Level Based Deep Learning Framework for Early Detection of Dysplasia in Oral Squamous Epithelium

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