Histopathological Diagnosis for Viable and Non-viable Tumor Prediction for Osteosarcoma Using Convolutional Neural Network

Mishra, Rashika; Daescu, Ovidiu; Leavey, Patrick J.; Rakheja, Dinesh; Sengupta, Anita

doi:10.1007/978-3-319-59575-7_2

Cited by 31 publications

(26 citation statements)

References 15 publications

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“…Colour augmentation is also investigated to take into account stain variations. It usually consists in random transformations applied in the RGB or HSV colour space (see e.g., Sirinukunwattana et al (2016Sirinukunwattana et al ( , 2017; Lafarge et al (2017)) or on principal components (Xu et al (2017a); Mishra et al (2017)). It should be noted that random variations in the RGB space should be small to prevent from producing aberrant colours out of the range of the H&E or IHC histological staining.…”

Section: Previous Work and Novel Contributionsmentioning

confidence: 99%

Segmentation of glandular epithelium in colorectal tumours to automatically compartmentalise IHC biomarker quantification: A deep learning approach

Eycke

Balsat

Verset

et al. 2018

Medical Image Analysis

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In this paper, we propose a method for automatically annotating slide images from colorectal tissue samples. Our objective is to segment glandular epithelium in histological images from tissue slides submitted to different staining techniques, including usual haematoxylin-eosin (H&E) as well as immunohistochemistry (IHC). The proposed method makes use of Deep Learning and is based on a new convolutional network architecture. Our method achieves better performances than the state of the art on the H&E images of the GlaS challenge contest, whereas it uses only the haematoxylin colour channel extracted by colour deconvolution from the RGB images in order to extend its applicability to IHC. The network only needs to be fine-tuned on a small number of additional examples to be accurate on a new IHC dataset. Our approach also includes a new method of data augmentation to achieve good generalisation when working with different experimental conditions and different IHC markers. We show that our methodology enables to automate the compartmentalisation of the IHC biomarker analysis, results concurring highly with manual annotations.

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Section: Previous Work and Novel Contributionsmentioning

confidence: 99%

Segmentation of glandular epithelium in colorectal tumours to automatically compartmentalise IHC biomarker quantification: A deep learning approach

Eycke

Balsat

Verset

et al. 2018

Medical Image Analysis

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“…To overcome the problem caused by small datasets, in the paper, a deep model with Siamese network (DS-Net) was designed to automatically classify osteosarcoma images from TCIA. 32 In recent years, some research literatures [33][34][35][36][37] have proposed some methods for histological classification in osteosarcoma using deep learning methods. It should be noted that the method in Ref.…”

Section: Introductionmentioning

confidence: 99%

Deep model with Siamese network for viable and necrotic tumor regions assessment in osteosarcoma

Fu¹

2020

Medical Physics

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To achieve automatic classification of viable and necrotic tumor regions in osteosarcoma, most of the existing deep learning methods can only design a simple model to prevent overfitting on small datasets, which leads to the weak ability of extracting image features and low accuracy of the models. In order to solve the above problem, a deep model with Siamese network (DS-Net) was designed in this paper. Methods: The DS-Net constructed on the basis of full convolutional networks is composed of an auxiliary supervision network (ASN) and a classification network. The construction of the ASN based on the Siamese network aims to solve the problem of a small training set (the main bottleneck of deep learning in medical images). It uses paired data as the input and updates the network through combined labels. The classification network uses the features extracted by the ASN to perform accurate classification. Results: Pathological diagnosis is the most accurate method to identify osteosarcoma. However, due to intraclass variation and interclass similarity, it is challenging for pathologists to accurately identify osteosarcoma. Through the experiments on hematoxylin and eosin (H&E)-stained osteosarcoma histology slides, the DS-Net we constructed can achieve an average accuracy of 95.1%. Compared with existing methods, the DS-Net performs best in the test dataset. Conclusions: The DS-Net we constructed can not only effectively realize the histological classification of osteosarcoma, but also be applicable to many other medical image classification tasks affected by small datasets.

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“…To further verify the applicability of the BSTriplet loss to other medical image modalities, additional experiments have been done on an osteosarcoma histology image dataset [ 48 , 49 , 50 ], which can be accessed from the cancer imaging archive (TCIA) [ 51 ]. There are three kinds of images in the osteosarcoma histology image dataset, as shown in Figure 14 .…”

Section: Resultsmentioning

confidence: 99%

Batch Similarity Based Triplet Loss Assembled into Light-Weighted Convolutional Neural Networks for Medical Image Classification

Huang

Zhou

Zhu

et al. 2021

Sensors

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In many medical image classification tasks, there is insufficient image data for deep convolutional neural networks (CNNs) to overcome the over-fitting problem. The light-weighted CNNs are easy to train but they usually have relatively poor classification performance. To improve the classification ability of light-weighted CNN models, we have proposed a novel batch similarity-based triplet loss to guide the CNNs to learn the weights. The proposed loss utilizes the similarity among multiple samples in the input batches to evaluate the distribution of training data. Reducing the proposed loss can increase the similarity among images of the same category and reduce the similarity among images of different categories. Besides this, it can be easily assembled into regular CNNs. To appreciate the performance of the proposed loss, some experiments have been done on chest X-ray images and skin rash images to compare it with several losses based on such popular light-weighted CNN models as EfficientNet, MobileNet, ShuffleNet and PeleeNet. The results demonstrate the applicability and effectiveness of our method in terms of classification accuracy, sensitivity and specificity.

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Histopathological Diagnosis for Viable and Non-viable Tumor Prediction for Osteosarcoma Using Convolutional Neural Network

Cited by 31 publications

References 15 publications

Segmentation of glandular epithelium in colorectal tumours to automatically compartmentalise IHC biomarker quantification: A deep learning approach

Segmentation of glandular epithelium in colorectal tumours to automatically compartmentalise IHC biomarker quantification: A deep learning approach

Deep model with Siamese network for viable and necrotic tumor regions assessment in osteosarcoma

Batch Similarity Based Triplet Loss Assembled into Light-Weighted Convolutional Neural Networks for Medical Image Classification

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