Microscopic medical image classification framework via deep learning and shearlet transform

Abstract

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.

Unsupervised Feature Extraction via Deep Learning for Histopathological Classification of Colon Tissue Images

“…For that, the majority of these studies train a CNN classifier in a supervised manner and exploit its output for classification or segmentation. Among these studies, only a few feed an entire tissue image to the trained CNN and use the class label it outputs to directly classify the image [4], [5]. Others divide a tissue image into a grid of patches, feed each patch to the CNN, which is also trained on the same-sized patches, and then use either the class labels or the posteriors generated by this CNN.…”

“…Our proposed method differs from the existing studies in the following aspects. The studies that use deep learning for histopathological image analysis either train a learner on entire images for their classification [4], [5] or crop small patches out of these images, train a learner on the patches and then use the patch labels for entire image classification [6], [7] but more typically for nucleus detection or entire image segmentation [8], [9], [10], [11], [12], [13]. On the other hand, as opposed to our proposed method, these studies either pick random points in an image as the patch centers, or divide the image into a grid, or use the sliding window approach.…”

Hyperspectral Imaging for the Detection of Glioblastoma Tumor Cells in H&E Slides Using Convolutional Neural Networks

“…Such technologies are intended to improve the diagnosis of pathological slides to be more reproducible, increase objectivity, and save time in the routine examination of samples [2,3]. Although most of the research carried out in computational pathology has been in the context of RGB (red, green, and blue) image analysis [4][5][6][7], hyperspectral (HS) and multispectral imaging are shown as promising technologies to aid in the histopathological analysis of samples.…”

Deep learning to automate Brasfield chest radiographic scoring for cystic fibrosis

“…In recent years, deep learning utilizing convolutional neural networks, a form of machine learning, has been successfully employed to accurately perform a variety of image recognition and classification tasks by encoding hierarchies of spatial features through adaptive mathematical models [11][12][13][14]. Emerging medical applications range from automated bone age assessment to automated CXR diagnosis [11][12][13][14][15][16][17][18][19][20][21]. Deep learning thus also has potential to automate Brasfield scoring, thereby reducing the need for subspecialized readers to perform tedious processes while maintaining reliable quantitative metrics.…”