Hierarchical task-driven feature learning for tumor histology

Couture, Heather D.; Marron, J. S.; Thomas, Nancy E.; Perou, Charles M.; Niethammer, Marc

doi:10.1109/isbi.2015.7164039

Cited by 2 publications

(2 citation statements)

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“…15 These methods often outperform traditional approaches that use hand-crafted features. [16][17][18][19] Cruz-Roa et al 15 proposed a three-layer convolutional neural network (CNN) method for invasive ductal carcinoma detection in histopathology images of breast cancer and compared their method with hand-crafted features. They reported 6% improvement in the classification accuracy when using their CNN.…”

Section: Introductionmentioning

confidence: 99%

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Microscopic medical image classification framework via deep learning and shearlet transform

2016

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Abstract. Cancer is the second leading cause of death in US after cardiovascular disease. Image-based computer-aided diagnosis can assist physicians to efficiently diagnose cancers in early stages. Existing computeraided algorithms use hand-crafted features such as wavelet coefficients, co-occurrence matrix features, and recently, histogram of shearlet coefficients for classification of cancerous tissues and cells in images. These hand-crafted features often lack generalizability since every cancerous tissue and cell has a specific texture, structure, and shape. An alternative approach is to use convolutional neural networks (CNNs) to learn the most appropriate feature abstractions directly from the data and handle the limitations of hand-crafted features. A framework for breast cancer detection and prostate Gleason grading using CNN trained on images along with the magnitude and phase of shearlet coefficients is presented. Particularly, we apply shearlet transform on images and extract the magnitude and phase of shearlet coefficients. Then we feed shearlet features along with the original images to our CNN consisting of multiple layers of convolution, max pooling, and fully connected layers. Our experiments show that using the magnitude and phase of shearlet coefficients as extra information to the network can improve the accuracy of detection and generalize better compared to the state-of-the-art methods that rely on hand-crafted features. This study expands the application of deep neural networks into the field of medical image analysis, which is a difficult domain considering the limited medical data available for such analysis.

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Section: Introductionmentioning

confidence: 99%

“…They reported 3% improvement in segmentation accuracy when using their DL framework. Couture et al 17 proposed a sparse coding-based hierarchical feature learning method for breast cancer detection in histopathology images. They were able to increase the classification accuracy by 6% using their proposed feature learning method.…”

Section: Introductionmentioning

confidence: 99%

Microscopic medical image classification framework via deep learning and shearlet transform

2016

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Deep Learning-Based Prediction of Molecular Tumor Biomarkers from H&E: A Practical Review

Couture¹

2022

JPM

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Molecular and genomic properties are critical in selecting cancer treatments to target individual tumors, particularly for immunotherapy. However, the methods to assess such properties are expensive, time-consuming, and often not routinely performed. Applying machine learning to H&E images can provide a more cost-effective screening method. Dozens of studies over the last few years have demonstrated that a variety of molecular biomarkers can be predicted from H&E alone using the advancements of deep learning: molecular alterations, genomic subtypes, protein biomarkers, and even the presence of viruses. This article reviews the diverse applications across cancer types and the methodology to train and validate these models on whole slide images. From bottom-up to pathologist-driven to hybrid approaches, the leading trends include a variety of weakly supervised deep learning-based approaches, as well as mechanisms for training strongly supervised models in select situations. While results of these algorithms look promising, some challenges still persist, including small training sets, rigorous validation, and model explainability. Biomarker prediction models may yield a screening method to determine when to run molecular tests or an alternative when molecular tests are not possible. They also create new opportunities in quantifying intratumoral heterogeneity and predicting patient outcomes.

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Hierarchical task-driven feature learning for tumor histology

Cited by 2 publications

References 11 publications

Microscopic medical image classification framework via deep learning and shearlet transform

Microscopic medical image classification framework via deep learning and shearlet transform

Deep Learning-Based Prediction of Molecular Tumor Biomarkers from H&E: A Practical Review

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