In recent years, deep learning has vastly improved the identification and diagnosis of various diseases in plants. In this report, we investigate the problem of pathology classification using images of a single leaf. We explore the use of standard benchmark models such as VGG16, ResNet101, and DenseNet 161 to achieve a 0.945 score on the task. Furthermore, we explore the use of the newer EfficientNet model, improving the accuracy to 0.962. Finally, we introduce the state-of-the-art idea of semi-supervised Noisy Student training to the EfficientNet, resulting in significant improvements in both accuracy and convergence rate. The final ensembled Noisy Student model performs very well on the task, achieving a test score of 0.982.
This paper presents a framework which uses computer vision algorithms to standardise images and analyse them for identifying crop diseases automatically. The tools are created to bridge the information gap between farmers, advisory call centres and agricultural experts using the images of diseased/infected crop captured by mobile-phones. These images are generally sensitive to a number of factors including camera type and lighting. We therefore propose a technique for standardising the colour of plant images within the context of the advisory system. Subsequently, to aid the advisory process, …
Apple orchards in the U.S. are under constant threat from a large number of pathogens and insects. Appropriate and timely deployment of disease management depends on early disease detection. Incorrect and delayed diagnosis can result in either excessive or inadequate use of chemicals, with increased production costs, environmental, and health impacts. We have manually captured 3,651 high-quality, real-life symptom images of multiple apple foliar diseases, with variable illumination, angles, surfaces, and noise. A subset, expert-annotated to create a pilot dataset for apple scab, cedar apple ru…
Shenzhen University（深圳大学）；The Ohio State University（俄亥俄州立大学）；University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）
In recent years, advances in the development of whole-slide images have laid a foundation for the utilization of digital images in pathology. With the assistance of computer images analysis that automatically identifies tissue or cell types, they have greatly improved the histopathologic interpretation and diagnosis accuracy. In this paper, the Convolutional Neutral Network (CNN) has been adapted to predict and classify lymph node metastasis in breast cancer. Unlike traditional image cropping methods that are only suitable for large resolution images, we propose a novel data augmentation metho…
University of Science and Technology of China（中国科学技术大学）；UNSW Sydney（新南威尔士大学）；Genmab Inc.（Genmab公司）
NLP-based computer vision models, particularly vision transformers, have been shown to outperform CNN models in many imaging tasks. However, most digital pathology artificial-intelligence models are based on CNN architectures, probably owing to a lack of data regarding NLP models for pathology images. In this study, we developed digital pathology pipelines to benchmark the five most recently proposed NLP models (vision transformer (ViT), Swin Transformer, MobileViT, CMT, and Sequencer2D) and four popular CNN models (ResNet18, ResNet50, MobileNetV2, and EfficientNet) to predict biomarkers in co…
DPSeq: A Novel and Efficient Digital Pathology Classifier for Predicting Cancer Biomarkers using Sequencer Architecture
School of Data Science, University of Science and Technology of China（中国科学技术大学数据科学学院）；School of Management, University of Science and Technology of China（中国科学技术大学管理学院）；School of Population Health, UNSW Sydney（新南威尔士大学悉尼分校人口健康学院）；Genmab Inc.（杰特贝公司）
Hybrid Convolution Neural Network Integrated with Pseudo-Newton Boosting for Lumbar Spine Degeneration Detection
School of Computer Science and Engineering, Vellore Institute of Technology（韦洛尔理工大学计算机科学与工程学院）；Department of Information Science and Technology, College of Engineering Guindy, Anna University（安娜大学金迪工程学院信息科学与技术系）；Integrated Management Coastal Zones Research Institute, Universitat Politecnica de Valencia（瓦伦西亚理工大学海岸带综合管理研究所）
Vanderbilt University（范德堡大学）；Vanderbilt University Medical Center（范德堡大学医学中心）
The classification of glomerular lesions is a routine and essential task in renal pathology. Recently, machine learning approaches, especially deep learning algorithms, have been used to perform computer-aided lesion characterization of glomeruli. However, one major challenge of developing such methods is the naturally imbalanced distribution of different lesions. In this paper, we propose CircleMix, a novel data augmentation technique, to improve the accuracy of classifying globally sclerotic glomeruli with a hierarchical learning strategy. Different from the recently proposed CutMix method, …
Deep Learning Segmentation and Classification of Red Blood Cells Using a Large Multi-Scanner Dataset
Department of Biomedical Engineering and Systems, Faculty of Engineering, Cairo University（开罗大学工程学院生物医学工程与系统系）；PathOlOgics, LLC（PathOlOgics有限责任公司）；School of Information Technology, New Giza University（新吉萨大学信息技术学院）
MedicalPatchNet: A Patch-Based Self-Explainable AI Architecture for Chest X-ray Classification
Department of Diagnostic and Interventional Radiology of the University Hospital RWTH Aachen（鲁尔大学亚琛医院诊断与介入放射科）；Else Kroener Fresenius Center for Digital Health, Technical University Dresden（德累斯顿技术大学埃勒克罗纳弗雷斯内尔数字健康中心）；Department of Medicine III, University Hospital RWTH Aachen（鲁尔大学亚琛医院第三医学部）；Pathology & Data Analytics, Leeds Institute of Medical Research at St James’s, University of Leeds（利兹大学圣詹姆斯医院医学研究所以及病理学与数据科学）；Department of Medicine I, University Hospital Dresden（德累斯顿大学第一医学部）；Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg（海德堡大学国家肿瘤疾病中心（NCT）肿瘤医学部）
AdapterMoE: A Two-Stage Hard-Routing Mixture-of-Experts Architecture for Multi-Crop Disease Recognition with Calibrated Rejection and Incremental Learning
