论文标题
使用深度学习方法对活检图像的乳糜泻和环境肠病的诊断和分析
Diagnosis and Analysis of Celiac Disease and Environmental Enteropathy on Biopsy Images using Deep Learning Approaches
论文作者
论文摘要
腹腔疾病（CD）和环境肠病（EE）是营养不良的常见原因，并对正常的儿童发展产生不利影响。两种情况都需要进行组织活检进行诊断，并且要解释临床活检图像以区分这些胃肠道疾病的主要挑战是它们之间的组织病理学重叠。在当前的研究中，我们提出了针对这些疾病的四种诊断技术，并解决了它们的局限性和优势。首先，考虑了CD，EE和正常活检之间的诊断，但是这种诊断技术的主要挑战是染色问题。本研究中使用的数据集是从具有不同染色标准的不同中心收集的。为了解决这个问题，我们使用颜色平衡来用不同的颜色训练我们的模型。随机多模型深度学习（RMDL）体系结构已被用作减轻染色问题影响的另一种方法。 RMDL结合了深度学习的不同架构和结构，模型的最终输出基于多数投票。 CD是一种慢性自身免疫性疾病，会影响小肠遗传易感儿童和成人。通常，CD从Marsh I到IIIA迅速发展。 Marsh III被细分为IIIA（部分绒毛萎缩），Marsh IIIB（小绒毛萎缩）和Marsh IIIC（总绒毛萎缩），以解释绒毛萎缩的光谱以及隐层肥大并增加了上皮淋巴细胞。在本研究的第二部分中，我们提出了两种诊断CD不同阶段的方法。最后，在本研究的第三部分中，这两个步骤被合并为分层医学图像分类（HMIC），以具有层次诊断疾病数据的模型。
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.
