Multi-task Deep Learning for Colon Cancer Grading

Vuong, Trinh Thi Le; Lee, Daigeun; Kwak, Jin Tae; Kim, Kyungeun

doi:10.1109/iceic49074.2020.9051305

Cited by 21 publications

(15 citation statements)

References 4 publications

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“…A histology image viewed at high magnification (typically 20x or 40x) can reveal millions of subtle cellular features, and deep CNN models are exceptionally good at extracting features from high-resolution image data [ 82 ]. Automating cancer grading with histology-based deep CNNs has proven successful, with studies showing that performance of deep CNNs can be comparable with pathologists in grading prostate [ 40 – 42 ], breast [ 43 ], colon cancer [ 44 ] and lymphoma [ 45 ]. Explainability methods can enable and improve histology-based classification models by allowing pathologists to validate DL-generated predictions.…”

Section: Deep Learning In Oncologymentioning

confidence: 99%

Deep learning in cancer diagnosis, prognosis and treatment selection

et al. 2021

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Deep learning is a subdiscipline of artificial intelligence that uses a machine learning technique called artificial neural networks to extract patterns and make predictions from large data sets. The increasing adoption of deep learning across healthcare domains together with the availability of highly characterised cancer datasets has accelerated research into the utility of deep learning in the analysis of the complex biology of cancer. While early results are promising, this is a rapidly evolving field with new knowledge emerging in both cancer biology and deep learning. In this review, we provide an overview of emerging deep learning techniques and how they are being applied to oncology. We focus on the deep learning applications for omics data types, including genomic, methylation and transcriptomic data, as well as histopathology-based genomic inference, and provide perspectives on how the different data types can be integrated to develop decision support tools. We provide specific examples of how deep learning may be applied in cancer diagnosis, prognosis and treatment management. We also assess the current limitations and challenges for the application of deep learning in precision oncology, including the lack of phenotypically rich data and the need for more explainable deep learning models. Finally, we conclude with a discussion of how current obstacles can be overcome to enable future clinical utilisation of deep learning.

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Section: Deep Learning In Oncologymentioning

confidence: 99%

Deep learning in cancer diagnosis, prognosis and treatment selection

et al. 2021

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“…They stated that they used DenseNET121 and obtained an accuracy of 85.91%. 16 Sitnik et al in their study stated that they found a trivial difference in performance between deep classifiers trained from scrape and corresponding classifiers previously trained on off-field image datasets, and showed that the best micro-balanced accuracy predicted by the U-NET ++ classifier in the independent test set was equal to 89.34%. F1 score and sensitivity were 83.67% and 81.11%, respectively.…”

Section: Literaturementioning

confidence: 99%

Classification with respect to colon adenocarcinoma and colon benign tissue of colon histopathological images with a new CNN model: MA_ColonNET

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Çınar

2021

Int J Imaging Syst Tech

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“…As a result, the GoogLeNet CNN gives higher performance (0.99 f1-score) than the shallow CNN (0.92 f1-score). The use of multi-task DL for colon cancer grading has been demonstrated by (Vuong et al, 2020) with a neural network consisting of DenseNet121 and two consecutive fully connected layers (classification and regression layers). Regression is applied to predict the values of a desired target quantity when the target quantity is continuous.…”

Section: Related Workmentioning

confidence: 99%

PancreaSys: An Automated Cloud-Based Pancreatic Cancer Grading System

Sehmi¹,

Fauzi²,

Ahmad³

et al. 2022

Front. Signal Process.

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Pancreatic cancer is one of the deadliest diseases which has taken millions of lives over the past 20 years. Due to challenges in grading pancreatic cancer, this study presents an automated cloud-based system, utilizing a convolutional neural network deep learning (DL) approach to classifying four classes of pancreatic cancer grade from pathology image into Normal, Grade I, Grade II, and Grade III. This cloud-based system, named PancreaSys, takes an input of high power field images from the web user interface, slices them into smaller patches, makes predictions, and stitches back the patches before returning the final result to the pathologist. Anvil and Google Colab are used as the backbone of the system to build a web user interface for deploying the DL model in the classification of the cancer grade. This work employs the transfer learning approach on a pre-trained DenseNet201 model with data augmentation to alleviate the small dataset’s challenges. A 5-fold cross-validation (CV) was employed to ensure all samples in a dataset were used to evaluate and mitigate selection bias during splitting the dataset into 80% training and 20% validation sets. The experiments were done on three different datasets (May Grunwald-Giemsa (MGG), hematoxylin and eosin (H&E), and a mixture of both, called the Mixed dataset) to observe the model performance on two different pathology stains (MGG and H&E). Promising performances are reported in predicting the pancreatic cancer grade from pathology images, with a mean f1-score of 0.88, 0.96, and 0.89 for the MGG, H&E, and Mixed datasets, respectively. The outcome from this research is expected to serve as a prognosis system for the pathologist in providing accurate grading for pancreatic cancer in pathological images.

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Multi-task Deep Learning for Colon Cancer Grading

Cited by 21 publications

References 4 publications

Deep learning in cancer diagnosis, prognosis and treatment selection

Deep learning in cancer diagnosis, prognosis and treatment selection

Classification with respect to colon adenocarcinoma and colon benign tissue of colon histopathological images with a new CNN model: MA_ColonNET

PancreaSys: An Automated Cloud-Based Pancreatic Cancer Grading System

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