Automated brain histology classification using machine learning

Ker, Justin; Bai, Yinjuan; Lee, Hwei Yee; Rao, Jai Prashanth; Wang, Lipo

doi:10.1016/j.jocn.2019.05.019

Cited by 132 publications

(63 citation statements)

References 17 publications

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“…Such pipelines have to be implemented and supervised carefully to ensure that errors don't propagate through the chain of algorithms. The present pipeline can eventually be expanded to include automatic classification of a BMs histology [28], prediction of treatment response [29] or to directly influence the treatment e.g. through dose optimization [30].…”

Section: Discussionmentioning

confidence: 99%

Deep convolutional neural networks for automated segmentation of brain metastases trained on clinical data

et al. 2020

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Introduction: Deep learning-based algorithms have demonstrated enormous performance in segmentation of medical images. We collected a dataset of multiparametric MRI and contour data acquired for use in radiosurgery, to evaluate the performance of deep convolutional neural networks (DCNN) in automatic segmentation of brain metastases (BM). Methods: A conventional U-Net (cU-Net), a modified U-Net (moU-Net) and a U-Net trained only on BM smaller than 0.4 ml (sU-Net) were implemented. Performance was assessed on a separate test set employing sensitivity, specificity, average false positive rate (AFPR), the dice similarity coefficient (DSC), Bland-Altman analysis and the concordance correlation coefficient (CCC). Results: A dataset of 509 patients (1223 BM) was split into a training set (469 pts) and a test set (40 pts). A combination of all trained networks was the most sensitive (0.82) while maintaining a specificity 0.83. The same model achieved a sensitivity of 0.97 and a specificity of 0.94 when considering only lesions larger than 0.06 ml (75% of all lesions). Type of primary cancer had no significant influence on the mean DSC per lesion (p = 0.60). Agreement between manually and automatically assessed tumor volumes as quantified by a CCC of 0.87 (95% CI, 0.77-0.93), was excellent. Conclusion: Using a dataset which properly captured the variation in imaging appearance observed in clinical practice, we were able to conclude that DCNNs reach clinically relevant performance for most lesions. Clinical applicability is currently limited by the size of the target lesion. Further studies should address if small targets are accurately represented in the test data.

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Section: Discussionmentioning

confidence: 99%

Deep convolutional neural networks for automated segmentation of brain metastases trained on clinical data

et al. 2020

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“…Inception v3 of the open source of Google™ was selected, which contains the module characteristics suitable for pathology tasks ( Ker et al, 2019 ). Inception v3 has been applied in classification tasks in skin cancer ( Esteva et al, 2017 ) and diabetic retinopathy ( Gulshan et al, 2016 ).…”

Section: Methodsmentioning

confidence: 99%

Artificial Intelligence-Based Multiclass Classification of Benign or Malignant Mucosal Lesions of the Stomach

Guo

Hu³

et al. 2020

Front. Pharmacol.

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Gastric cancer (GC) is one of the leading causes of cancer-related death worldwide. It takes some time from chronic gastritis to develop in GC. Early detection of GC will help patients obtain timely treatment. Understanding disease evolution is crucial for the prevention and treatment of GC. Here, we present a convolutional neural network (CNN)-based system to detect abnormalities in the gastric mucosa. We identified normal mucosa, chronic gastritis, and intestinal-type GC: this is the most common route of gastric carcinogenesis. We integrated digitalizing histopathology of whole-slide images (WSIs), stain normalization, a deep CNN, and a random forest classifier. The staining variability of WSIs was reduced significantly through stain normalization, and saved the cost and time of preparing new slides. Stain normalization improved the effect of the CNN model. The accuracy rate at the patch-level reached 98.4%, and 94.5% for discriminating normal → chronic gastritis → GC. The accuracy rate at the WSIs-level for discriminating normal tissue and cancerous tissue reached 96.0%, which is a state-of-the-art result. Survival analyses indicated that the features extracted from the CNN exerted a significant impact on predicting the survival of cancer patients. Our CNN model disclosed significant potential for adjuvant diagnosis of gastric diseases, especially GC, and usefulness for predicting the prognosis.

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“…is the regularization term to prevent over-fitting of the displacement field  , and the common norm 2 L is employed.  is the regularization coefficient.…”

Section: ) the Loss Functionmentioning

confidence: 99%

“…Deep learning has enabled the most advanced performance of many computer vision tasks, including but not limited to target detection, feature extraction, image classification, image de-noising and image reconstruction [1][2][3][4]. It has also led to huge improvements in medical image processing [5][6][7][8] , allowing expert diagnosis of individual diseases.…”

Section: Introductionmentioning

confidence: 99%

A CNN-Based Approach for Lung 3D-CT Registration

Yang

2020

IEEE Access

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Deep learning techniques have been applied to certain rigid or non-rigid medical image registration due to its potential advantages in meeting the clinical requirements of real-time and accuracy. Based on the deep learning model, this study aims to explore specific network models suitable for lung CT images. The proposed model took unlabeled 3D image pairs as input, and the convolutional neural network (CNN) was utilized and identified as a function with ability of sharing parameters to obtain displacement field. The image pair could be aligned by applying the acquired displacement field to the target image through spatial transformation. The similarity between the aligned image pair combined with the constraints on the displacement field was taken as the objective function to obtain the optimal parameters. Two models with different depths were designed and the consequent registration effects with different optimization methods and convolution kernel sizes were explored. The results proved that the designs with deeper level using Adam optimizer and smaller convolution kernels in obtaining displacement fields had higher accuracy and stronger robustness. The accuracy of the unsupervised model was comparable to state-of-theart methods, while operating orders of magnitude faster. This study proposed a feasible registration method for lung 3D-CT, and its usefulness in aligning CT images has been demonstrated.

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Automated brain histology classification using machine learning

Cited by 132 publications

References 17 publications

Deep convolutional neural networks for automated segmentation of brain metastases trained on clinical data

Deep convolutional neural networks for automated segmentation of brain metastases trained on clinical data

Artificial Intelligence-Based Multiclass Classification of Benign or Malignant Mucosal Lesions of the Stomach

A CNN-Based Approach for Lung 3D-CT Registration

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