Multiclass Tissue Classification of Whole-Slide Histological Images using Convolutional Neural Networks

Wetteland, Rune; Engan, Kjersti; Eftestøl, Trygve; Kvikstad, Vebjørn; Janssen, E. A. M.

doi:10.5220/0007253603200327

Cited by 15 publications

(23 citation statements)

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“…A disadvantage of this is that one image must be created for each class. We earlier showed this approach in Wetteland et al, 25 but have omitted it from this paper.…”

Section: Methodsmentioning

confidence: 94%

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A Multiscale Approach for Whole-Slide Image Segmentation of five Tissue Classes in Urothelial Carcinoma Slides

Wetteland

Engan

Eftestøl

et al. 2020

Technol Cancer Res Treat

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In pathology labs worldwide, we see an increasing number of tissue samples that need to be assessed without the same increase in the number of pathologists. Computational pathology, where digital scans of histological samples called whole-slide images (WSI) are processed by computational tools, can be of help for the pathologists and is gaining research interests. Most research effort has been given to classify slides as being cancerous or not, localization of cancerous regions, and to the “big-four” in cancer: breast, lung, prostate, and bowel. Urothelial carcinoma, the most common form of bladder cancer, is expensive to follow up due to a high risk of recurrence, and grading systems have a high degree of inter- and intra-observer variability. The tissue samples of urothelial carcinoma contain a mixture of damaged tissue, blood, stroma, muscle, and urothelium, where it is mainly muscle and urothelium that is diagnostically relevant. A coarse segmentation of these tissue types would be useful to i) guide pathologists to the diagnostic relevant areas of the WSI, and ii) use as input in a computer-aided diagnostic (CAD) system. However, little work has been done on segmenting tissue types in WSIs, and on computational pathology for urothelial carcinoma in particular. In this work, we are using convolutional neural networks (CNN) for multiscale tile-wise classification and coarse segmentation, including both context and detail, by using three magnification levels: 25x, 100x, and 400x. 28 models were trained on weakly labeled data from 32 WSIs, where the best model got an F1-score of 96.5% across six classes. The multiscale models were consistently better than the single-scale models, demonstrating the benefit of combining multiple scales. No tissue-class ground-truth for complete WSIs exist, but the best models were used to segment seven unseen WSIs where the results were manually inspected by a pathologist and are considered as very promising.

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“…A disadvantage of this is that one image must be created for each class. We earlier showed this approach in Wetteland et al, 25 but have omitted it from this paper.…”

Section: Methodsmentioning

confidence: 94%

“…In Wetteland et al, 25 we presented a method based on convolutional neural networks (CNN) for classifying tiles of urothelial carcinoma WSI into the six classes shown in Figure 1 . The model utilized the autoencoder architecture and was first pre-trained on a large unlabeled dataset, and afterward fine-tuned on an annotated dataset.…”

Section: Introductionmentioning

confidence: 99%

A Multiscale Approach for Whole-Slide Image Segmentation of five Tissue Classes in Urothelial Carcinoma Slides

Wetteland

Engan

Eftestøl

et al. 2020

Technol Cancer Res Treat

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“…Previous work from our group, on bladder cancer, included tissue segmentation [13] [27] [28], and prediction of recurrence in NMIBC patients [29]. In Wetteland et al [13], we experimented with three magnification scales and any combination of these.…”

Section: Previous Workmentioning

confidence: 99%

Automatic Diagnostic Tool for Predicting Cancer Grade in Bladder Cancer Patients Using Deep Learning

et al. 2021

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The most common type of bladder cancer is urothelial carcinoma, which is among the cancer types with the highest recurrence rate and lifetime treatment cost per patient. Diagnosed patients are stratified into risk groups, mainly based on grade and stage. However, it is well known that correct grading of bladder cancer suffers from intra-and interobserver variability and inconsistent reproducibility between pathologists, potentially leading to under-or overtreatment of the patients. The economic burden, unnecessary patient suffering, and additional load on the health care system illustrate the importance of developing new tools to aid pathologists. We propose a pipeline, called TRI grade , that will identify diagnostic relevant regions in the whole-slide image (WSI) and collectively predict the grade of the current WSI. The system consists of two main models, trained on weak slide-level grade labels. First, a WSI is segmented into the different tissue classes (urothelium, stroma, muscle, blood, damaged tissue, and background). Next, tiles are extracted from the diagnostic relevant urothelium tissue from three magnification levels (25x, 100x, and 400x) and processed sequentially by a convolutional neural network (CNN) based model. Ten models were trained for the slide-level grading experiment, where the best model achieved an F1-score of 0.90 on a test set consisting of 50 WSIs. The best model was further evaluated on a smaller segmentation test set, consisting of 14 WSIs where low-and high-grade regions were annotated by a pathologist. The TRI grade pipeline achieved an average F1-score of 0.91 for both the low-grade and high-grade classes.INDEX TERMS Automated cancer grading, bladder cancer, convolutional neural networks, multiscale classification, urothelial carcinoma, weakly labelled data, whole-slide image.

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“…An accurate bladder cancer diagnosis supposes a very time-consuming task for expert pathologists, whose level of reproducibility is low enough to provide significant differences in the histological-based interpretation [12,13]. For that reason, many studies in the state of the art have proposed artificial-intelligence algorithms to help pathologists in terms of cost-effectiveness and subjectivity ratio.…”

Section: Related Workmentioning

confidence: 99%

A Novel Self-Learning Framework for Bladder Cancer Grading Using Histopathological Images

Garcı́a¹,

Esteve²,

Colomer³

et al. 2021

Preprint

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Recently, bladder cancer has been significantly increased in terms of incidence and mortality. Currently, two subtypes are known based on tumour growth: non-muscle invasive (NMIBC) and muscle-invasive bladder cancer (MIBC). In this work, we focus on the MIBC subtype because it is of the worst prognosis and can spread to adjacent organs. We present a self-learning framework to grade bladder cancer from histological images stained via immunohistochemical techniques. Specifically, we propose a novel Deep Convolutional Embedded Attention Clustering (DCEAC) which allows classifying histological patches into different severity levels of the disease, according to the patterns established in the literature. The proposed DCEAC model follows a two-step fully unsupervised learning methodology to discern between non-tumour, mild and infiltrative patterns from high-resolution samples of 512 × 512 pixels. Our system outperforms previous clustering-based methods by including a convolutional attention module, which allows refining the features of the latent space before the classification stage. The proposed network exceeds state-of-the-art approaches by 2−3% across different metrics, achieving a final average accuracy of 0.9034 in a multi-class scenario. Furthermore, the reported class activation maps evidence that our model is able to learn by itself the same patterns that clinicians consider relevant, without incurring prior annotation steps. This fact supposes a breakthrough in muscle-invasive bladder cancer grading which bridges the gap with respect to train the model on labelled data.

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Multiclass Tissue Classification of Whole-Slide Histological Images using Convolutional Neural Networks

Cited by 15 publications

References 0 publications

A Multiscale Approach for Whole-Slide Image Segmentation of five Tissue Classes in Urothelial Carcinoma Slides

A Multiscale Approach for Whole-Slide Image Segmentation of five Tissue Classes in Urothelial Carcinoma Slides

Automatic Diagnostic Tool for Predicting Cancer Grade in Bladder Cancer Patients Using Deep Learning

A Novel Self-Learning Framework for Bladder Cancer Grading Using Histopathological Images

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