Celiac Disease (CD) and Environmental Enteropathy (EE) are common causes of malnutrition and adversely impact normal childhood development. Both conditions require a tissue biopsy for diagnosis and a major challenge of interpreting clinical biopsy images to differentiate between these gastrointestinal diseases is striking histopathologic overlap between them. In the current study, we propose four diagnosis techniques for these diseases and address their limitations and advantages. First, the diagnosis between CD, EE, and Normal biopsies is considered, but the main challenge with this diagnosis technique is the staining problem. The dataset used in this research is collected from different centers with different staining standards. To solve this problem, we use color balancing in order to train our model with a varying range of colors. Random Multimodel Deep Learning (RMDL) architecture has been used as another approach to mitigate the effects of the staining problem. RMDL combines different architectures and structures of deep learning and the final output of the model is based on the majority vote. CD is a chronic autoimmune disease that affects the small intestine genetically predisposed children and adults. Typically, CD rapidly progress from Marsh I to IIIa. Marsh III is sub-divided into IIIa (partial villus atrophy), Marsh IIIb (subtotal villous atrophy), and Marsh IIIc (total villus atrophy) to explain the spectrum of villus atrophy along with crypt hypertrophy and increased intraepithelial lymphocytes. In the second part of this study, we proposed two ways for diagnosing different stages of CD. Finally, in the third part of this study, these two steps are combined as Hierarchical Medical Image Classification (HMIC) to have a model to diagnose the disease data hierarchically.
Image classification is central to the big data revolution in medicine. Improved information processing methods for diagnosis and classification of digital medical images have shown to be successful via deep learning approaches. As this field is explored, there are limitations to the performance of traditional supervised classifiers. This paper outlines an approach that is different from the current medical image classification tasks that view the issue as multi-class classification. We performed a hierarchical classification using our Hierarchical Medical Image classification (HMIC) approach. HMIC uses stacks of deep learning models to give particular comprehension at each level of the clinical picture hierarchy. For testing our performance, we use biopsy of the small bowel images that contain three categories in the parent level (Celiac Disease, Environmental Enteropathy, and histologically normal controls). For the child level, Celiac Disease Severity is classified into 4 classes (I, IIIa, IIIb, and IIIC).
Celiac Disease (CD) and Environmental Enteropathy (EE) are common causes of malnutrition and adversely impact normal childhood development. CD is an autoimmune disorder that is prevalent worldwide and is caused by an increased sensitivity to gluten. Gluten exposure destructs the small intestinal epithelial barrier, resulting in nutrient mal-absorption and childhood under-nutrition. EE also results in barrier dysfunction but is thought to be caused by an increased vulnerability to infections. EE has been implicated as the predominant cause of under-nutrition, oral vaccine failure, and impaired cognitive development in low-and-middle-income countries. Both conditions require a tissue biopsy for diagnosis, and a major challenge of interpreting clinical biopsy images to differentiate between these gastrointestinal diseases is striking histopathologic overlap between them. In the current study, we propose a convolutional neural network (CNN) to classify duodenal biopsy images from subjects with CD, EE, and healthy controls. We evaluated the performance of our proposed model using a large cohort containing 1000 biopsy images. Our evaluations show that the proposed model achieves an area under ROC of 0.99, 1.00, and 0.97 for CD, EE, and healthy controls, respectively. These results demonstrate the discriminative power of the proposed model in duodenal biopsies classification.
Deep convolutional neural networks(CNNs) have been successful for a wide range of computer vision tasks, including image classification. A specific area of the application lies in digital pathology for pattern recognition in the tissue-based diagnosis of gastrointestinal(GI) diseases. This domain can utilize CNNs to translate histopathological images into precise diagnostics. This is challenging since these complex biopsies are heterogeneous and require multiple levels of assessment. This is mainly due to structural similarities in different parts of the GI tract and shared features among different gut diseases. Addressing this problem with a flat model that assumes all classes (parts of the gut and their diseases) are equally difficult to distinguish leads to an inadequate assessment of each class. Since the hierarchical model restricts classification error to each sub-class, it leads to a more informative model than a flat model. In this paper, we propose to apply the hierarchical classification of biopsy images from different parts of the GI tract and the receptive diseases within each. We embedded a class hierarchy into the plain VGGNet to take advantage of its layers' hierarchical structure. The proposed model was evaluated using an independent set of image patches from 373 whole slide images. The results indicate that the hierarchical model can achieve better results than the flat model for multi-category diagnosis of GI disorders using histopathological images.
Ström, Peter, Kartasalo, Kimmo, Olsson, Henrik, Solorzano, Leslie, Delahunt, Brett, Berney, Daniel M., Bostwick, David G., Evans, Andrew J., Grignon, David J., Humphrey, Peter A., Iczkowski, Kenneth A., Kench, James G., Kristiansen, Glen, van der Kwast, Theodorus H., Leite, Katia R. M., McKenney, Jesse K., Oxley, Jon, Pan, Chin-Chen, Samaratunga, Hemamali, Srigley, John R., Takahashi, Hiroyuki, Tsuzuki, Toyonori, Varma, Murali, Zhou, Ming, Lindberg, Johan, Bergström, Cecilia, Ruusuvuori, Pekka, Wählby, Carolina, Grönberg, Henrik, Rantalainen, Mattias, Egevad, Lars, Eklund, Martin
Background: An increasing volume of prostate biopsies and a world-wide shortage of uro-pathologists puts a strain on pathology departments. Additionally, the high intra- and inter-observer variability in grading can result in over- and undertreatment of prostate cancer. Artificial intelligence (AI) methods may alleviate these problems by assisting pathologists to reduce workload and harmonize grading. Methods: We digitized 6,682 needle biopsies from 976 participants in the population based STHLM3 diagnostic study to train deep neural networks for assessing prostate biopsies. The networks were evaluated by predicting the presence, extent, and Gleason grade of malignant tissue for an independent test set comprising 1,631 biopsies from 245 men. We additionally evaluated grading performance on 87 biopsies individually graded by 23 experienced urological pathologists from the International Society of Urological Pathology. We assessed discriminatory performance by receiver operating characteristics (ROC) and tumor extent predictions by correlating predicted millimeter cancer length against measurements by the reporting pathologist. We quantified the concordance between grades assigned by the AI and the expert urological pathologists using Cohen's kappa. Results: The performance of the AI to detect and grade cancer in prostate needle biopsy samples was comparable to that of international experts in prostate pathology. The AI achieved an area under the ROC curve of 0.997 for distinguishing between benign and malignant biopsy cores, and 0.999 for distinguishing between men with or without prostate cancer. The correlation between millimeter cancer predicted by the AI and assigned by the reporting pathologist was 0.96. For assigning Gleason grades, the AI achieved an average pairwise kappa of 0.62. This was within the range of the corresponding values for the expert pathologists (0.60 to 0.73).