Digital histology images are amenable to the application of convolutional neural network (CNN) for analysis due to the sheer size of pixel data present in them. CNNs are generally used for representation learning from small image patches (e.g. 224x224) extracted from digital histology images due to computational and memory constraints. However, this approach does not incorporate high-resolution contextual information in histology images. We propose a novel way to incorporate larger context by a context-aware neural network based on images with a dimension of 1,792x1,792 pixels. The proposed framework first encodes the local representation of a histology image into high dimensional features then aggregates the features by considering their spatial organization to make a final prediction. The proposed method is evaluated for colorectal cancer grading and breast cancer classification. A comprehensive analysis of some variants of the proposed method is presented. Our method outperformed the traditional patch-based approaches, problem-specific methods, and existing context-based methods quantitatively by a margin of 3.61 . Code and dataset related information is available at this link: this https URL
In literature, various different approaches have been presented to incorporate the contextual information for the classification of histology images. Some researchers @cite_28 @cite_29 @cite_7 used image down-sampling, a common practice followed in natural image classification, to capture the context from larger histology image. However, this approach is not suitable for problems where cell information is as important as the context. Adaptive patch sampling @cite_18 and discriminative patch selection @cite_21 from histology images is another way to integrate the sparse context. These methods are not capable of capturing small regions of interest at high resolution e.g. tumor cells and their local contextual arrangement. Some methods @cite_6 @cite_23 @cite_9 @cite_17 leverage the multi-resolution nature of histology images and use multi-resolution based classifiers to capture context. These multi-resolution approaches only consider a small part of an image at high resolution and the remaining part at low resolutions to make a prediction. Therefore, these approaches lack the contextual information of cellular architecture at high resolution in a histology image.
@cite_18 @cite_7 @cite_28 @cite_29 @cite_21 @cite_9 @cite_6 @cite_23 @cite_17
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Breast cancer is one of the most commonly occurring types of cancer and the treatment administered to a subject is dependent on the grade or type of the lesion. In this manuscript, we make use of an ensemble of convolutional neural networks (CNN) to classify histology images as Normal, In-situ, Benign or Invasive. The performance of CNN is dependent on the network architecture, number of training instances and also on the data normalization scheme. However, there exists neither a single architecture nor a pre-processing regime that promises best performance. For the reason stated above, we use 3 CNNs trained on different pre-processing regimes to form an ensemble. On the held out test data (n = 40), the proposed scheme achieved an accuracy of 97.5 . On the challenge data (n = 100) provided by the organizers, the proposed technique achieved an accuracy of 87 and was jointly adjudged as the top performing algorithm for the task of classification of breast cancer from histology images. Each year, the treatment decisions for more than 230,000 breast cancer patients in the U.S. hinge on whether the cancer has metastasized away from the breast. Metastasis detection is currently performed by pathologists reviewing large expanses of biological tissues. This process is labor intensive and error-prone. We present a framework to automatically detect and localize tumors as small as 100 x 100 pixels in gigapixel microscopy images sized 100,000 x 100,000 pixels. Our method leverages a convolutional neural network (CNN) architecture and obtains state-of-the-art results on the Camelyon16 dataset in the challenging lesion-level tumor detection task. At 8 false positives per image, we detect 92.4 of the tumors, relative to 82.7 by the previous best automated approach. For comparison, a human pathologist attempting exhaustive search achieved 73.2 sensitivity. We achieve image-level AUC scores above 97 on both the Camelyon16 test set and an independent set of 110 slides. In addition, we discover that two slides in the Camelyon16 training set were erroneously labeled normal. Our approach could considerably reduce false negative rates in metastasis detection. Identifying lung adenocarcinoma growth patterns is critical for diagnosis and treatment of lung cancer patients. Growth patterns have variable texture, shape, size and location. They could appear individually or fused together in a way that makes it difficult to avoid inter intra variability in pathologists reports. Thus, employing a machine learning method to learn these patterns and automatically locate them within the tumour is indeed necessary. This will reduce the effort, assessment variability and provide a second opinion to support pathologies decision. To the best of our knowledge, no work has been done to classify growth patterns in lung adenocarcinoma. In this paper, we propose applying deep learning framework to perform lung adenocarcinoma pattern classification. We investigate what contextual information is adequate for training using patches extracted at several resolutions. We find that both cellular and architectural morphology features are required to achieve the best performance. Therefore, we propose using multi-resolution deep CNN for growth pattern classification in lung adenocarcinoma. Our preliminarily results show an increase in the overall classification accuracy. Abstract Accurate subtyping of ovarian carcinomas is an increasingly critical and often challenging diagnostic process. This work focuses on the development of an automatic classification model for ovarian carcinoma subtyping. Specifically, we present a novel clinically inspired contextual model for histopathology image subtyping of ovarian carcinomas. A whole slide image is modelled using a collection of tissue patches extracted at multiple magnifications. An efficient and effective feature learning strategy is used for feature representation of a tissue patch. The locations of salient, discriminative tissue regions are treated as latent variables allowing the model to explicitly ignore portions of the large tissue section that are unimportant for classification. These latent variables are considered in a structured formulation to model the contextual information represented from the multi-magnification analysis of tissues. A novel, structured latent support vector machine formulation is defined and used to combine information from multiple magnifications while simultaneously operating within the latent variable framework. The structural and contextual nature of our method addresses the challenges of intra-class variation and pathologists’ workload, which are prevalent in histopathology image classification. Extensive experiments on a dataset of 133 patients demonstrate the efficacy and accuracy of the proposed method against state-of-the-art approaches for histopathology image classification. We achieve an average multi-class classification accuracy of 90 , outperforming existing works while obtaining substantial agreement with six clinicians tested on the same dataset. Breast cancer (BC) is the second most leading cause of cancer deaths in women and BC metastasis accounts for the majority of deaths. Early detection of breast cancer metastasis in sentinel lymph nodes is of high importance for prediction and management of breast cancer progression. In this paper, we propose a novel deep learning framework for automatic detection of micro- and macro- metastasis in multi-gigapixel whole-slide images (WSIs) of sentinel lymph nodes. One of our main contributions is to incorporate a Bayesian solution for the optimization of network’s hyperparameters on one of the largest histology dataset, which leads to 5 gain in overall patch-based accuracy. Furthermore, we present an ensemble of two multi-resolution deep learning networks, one captures the cell level information and the other incorporates the contextual information to make the final prediction. Finally, we propose a two-step thresholding method to post-process the output of ensemble network. We evaluate our proposed method on the CAMELYON16 dataset, where we outperformed “human experts” and achieved the second best performance compared to 32 other competing methods.