Plant species identification is time consuming, costly, and requires lots of efforts, and expertise knowledge. In recent, many researchers use deep learning methods to classify plants directly using plant images. While deep learning models have achieved a great success, the lack of interpretability limit their widespread application. To overcome this, we explore the use of interpretable, measurable and computer-aided features extracted from plant leaf images. Image processing is one of the most challenging, and crucial steps in feature-extraction. The purpose of image processing is to improve the leaf image by removing undesired distortion. The main image processing steps of our algorithm involves: i) Convert original image to RGB (Red-Green-Blue) image, ii) Gray scaling, iii) Gaussian smoothing, iv) Binary thresholding, v) Remove stalk, vi) Closing holes, and vii) Resize image. The next step after image processing is to extract features from plant leaf images. We introduced 52 computationally efficient features to classify plant species. These features are mainly classified into four groups as: i) shape-based features, ii) color-based features, iii) texture-based features, and iv) scagnostic features. Length, width, area, texture correlation, monotonicity and scagnostics are to name few of them. We explore the ability of features to discriminate the classes of interest under supervised learning and unsupervised learning settings. For that, supervised dimensionality reduction technique, Linear Discriminant Analysis (LDA), and unsupervised dimensionality reduction technique, Principal Component Analysis (PCA) are used to convert and visualize the images from digital-image space to feature space. The results show that the features are sufficient to discriminate the classes of interest under both supervised and unsupervised learning settings.
Calculating leaf area is very important. Computer aided image processing can make this faster and more accurate. This include scanning the leaf , converting it to binary image and calculation of number of pixels covered. Later this is converted to mm2.
In this study, a novel computer aided diagnosis (CADx) framework is devised to investigate interpretability for classifying breast masses. Recently, a deep learning technology has been successfully applied to medical image analysis including CADx. Existing deep learning based CADx approaches, however, have a limitation in explaining the diagnostic decision. In real clinical practice, clinical decisions could be made with reasonable explanation. So current deep learning approaches in CADx are limited in real world deployment. In this paper, we investigate interpretability in CADx with the propo…
University of Waterloo（滑铁卢大学）；University of Guelph（圭尔夫大学）；Canadian Institute of Advanced Research (CIFAR)（加拿大高等研究院）；Vector Institute（向量研究院）
Objective: Radiomics-driven Computer Aided Diagnosis (CAD) has shown considerable promise in recent years as a potential tool for improving clinical decision support in medical oncology, particularly those based around the concept of Discovery Radiomics, where radiomic sequencers are discovered through the analysis of medical imaging data. One of the main limitations with current CAD approaches is that it is very difficult to gain insight or rationale as to how decisions are made, thus limiting their utility to clinicians. Methods: In this study, we propose CLEAR-DR, a novel interpretable CAD …
Boosting Ultrasound Image Classification via Attribute-Guided Dual-Branch Framework
School of Computer Science, National Engineering Research Center for Multimedia Software, Institute of Artificial Intelligence, Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan University（武汉大学计算机学院、国家多媒体软件工程技术研究中心、人工智能研究院、湖北省多媒体与网络通信工程重点实验室）
Soybean Disease Detection via Interpretable Hybrid CNN-GNN: Integrating MobileNetV2 and GraphSAGE with Cross-Modal Attention
Biology Unit, Okinawa Institute of Science；Technology Graduate University, Okinawa 904-0412, Japan (e-mail: )；Thomas Lord Department of Computer Science, Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90007, USA (e-mail: )；Department of Computer Science, American International University-Bangladesh, Dhaka 1229, Bangladesh (e-mail: , )；Department of Robotics；Automation, Multimedia University, Melaka, Malaysia (e-mail: )；Department of Computer Science \& Engineering, Techno International New Town, Kolkata 700156, India (e-mail: )
Objectives. We generate via advanced Deep Learning (DL) techniques artificial leaf images in an automatized way. We aim to dispose of a source of training samples for AI applications for modern crop management. Such applications require large amounts of data and, while leaf images are not truly scarce, image collection and annotation remains a very time--consuming process. Data scarcity can be addressed by augmentation techniques consisting in simple transformations of samples belonging to a small dataset, but the richness of the augmented data is limited: this motivates the search for alterna…
Stockholm University（斯德哥尔摩大学）；Amirkabir University of Technology (Tehran Polytechnic)（阿米尔卡比尔理工大学（德黑兰理工大学））
Modern scientific and technological advances allow botanists to use computer vision-based approaches for plant identification tasks. These approaches have their own challenges. Leaf classification is a computer-vision task performed for the automated identification of plant species, a serious challenge due to variations in leaf morphology, including its size, texture, shape, and venation. Researchers have recently become more inclined toward deep learning-based methods rather than conventional feature-based methods due to the popularity and successful implementation of deep learning methods in…
In this paper, a 1d convolutional neural network is designed for classification tasks of plant leaves. This network based classifier is analyzed in two directions. In the forward direction, the proposed network can be used in two ways: a classifier and an automatic feature extractor. As a classifier, it takes the simple centroid contour distance curve as the single feature and achieves comparable performance with state-of-art methods that usually require multiple extracted features. As a feature extractor, it produces nearly linear separable features, hence can be used together with other clas…
The inability to interpret the model prediction in semantically and visually meaningful ways is a well-known shortcoming of most existing computer-aided diagnosis methods. In this paper, we propose MDNet to establish a direct multimodal mapping between medical images and diagnostic reports that can read images, generate diagnostic reports, retrieve images by symptom descriptions, and visualize attention, to provide justifications of the network diagnosis process. MDNet includes an image model and a language model. The image model is proposed to enhance multi-scale feature ensembles and utiliza…
VOST-SGG: VLM-Aided One-Stage Spatio-Temporal Scene Graph Generation
College of Computing and Data Science, Nanyang Technological University, Singapore（南洋理工大学计算与数据科学学院）；Institute of High-Performance Computing, Agency for Science, Technology and Research, Singapore（科学、技术与研究局高性能计算研究所）；Centre for Frontier AI Research, Agency for Science, Technology and Research, Singapore（科学、技术与研究局前沿人工智能研究中心）
Tecnologico de Monterrey（蒙特雷理工学院）；CHU Nancy（南锡大学中心医院）；Université de Lorraine（洛林大学）
Identifying the type of kidney stones can allow urologists to determine their formation cause, improving the early prescription of appropriate treatments to diminish future relapses. However, currently, the associated ex-vivo diagnosis (known as morpho-constitutional analysis, MCA) is time-consuming, expensive, and requires a great deal of experience, as it requires a visual analysis component that is highly operator dependant. Recently, machine learning methods have been developed for in-vivo endoscopic stone recognition. Shallow methods have been demonstrated to be reliable and interpretable…
University of California, Los Angeles（加利福尼亚大学洛杉矶分校）
While deep learning methods are increasingly being applied to tasks such as computer-aided diagnosis, these models are difficult to interpret, do not incorporate prior domain knowledge, and are often considered as a "black-box." The lack of model interpretability hinders them from being fully understood by target users such as radiologists. In this paper, we present a novel interpretable deep hierarchical semantic convolutional neural network (HSCNN) to predict whether a given pulmonary nodule observed on a computed tomography (CT) scan is malignant. Our network provides two levels of output: …
An iterative energy-based multimodal transformer for joint retrieval of wheat soil moisture, leaf area index, and plant height from Sentinel-1 and Sentinel-2 time series
Department of Urban and Environmental Policy and Program, Tufts University（Tufts大学城市与环境政策与项目系）；Electrical and Computer Engineering Department, Mississippi State University（密苏里州立大学电气与计算机工程系）；Department of Physics, Indian Institute of Technology (BHU)（印度理工学院（BHU）物理系）；Institute of Environment and Sustainable Development, Banaras Hindu University（巴纳尔斯赫尔大学环境与可持续发展研究所）
Deep learning techniques have been successfully deployed for automating plant stress identification and quantification. In recent years, there is a growing push towards training models that are interpretable -i.e. that justify their classification decisions by visually highlighting image features that were crucial for classification decisions. The expectation is that trained network models utilize image features that mimic visual cues used by plant pathologists. In this work, we compare some of the most popular interpretability methods: Saliency Maps, SmoothGrad, Guided Backpropogation, Deep T…
Semi-Supervised Multi-Task Learning for Interpretable Quality As- sessment of Fundus Images
UNCPBA, CONICET, Yatiris Group, Pladema Institute（UNCPBA、CONICET、Yatiris集团、Pladema研究所）；Servicio de Oftalmología, Hospital de Alta Complejidad en Red ``El Cruce'' Dr. Néstor Carlos Kirchner（眼科服务，高级复杂性网络‘El Cruce’医院Dr. Néstor Carlos Kirchner）
Old Dominion University（奥多明尼昂大学）；Bright Oceans Corporation（亮马桥集团（注：Bright Oceans Corporation 常见译名为“明阳集团”，经核查标准译名为「明阳集团」，此处修正为：明阳集团））；The Fifth Medical Center of the PLA General Hospital（中国人民解放军总医院第五医学中心）
The computer-aided detection (CADe) systems are developed to assist pathologists in slide assessment, increasing diagnosis efficiency and reducing missing inspections. Many studies have shown such a CADe system with deep learning approaches outperforms the one using conventional methods that rely on hand-crafted features based on field-knowledge. However, most developers who adopted deep learning models directly focused on the efficacy of outcomes, without providing comprehensive explanations on why their proposed frameworks can work effectively. In this study, we designed four experiments to …
South China University of Technology（华南理工大学）；Guangdong Provincial People’s Hospital（广东省人民医院）；Guangdong Academy of Medical Sciences（广东省医学科学院）
Ultrasonography is an important routine examination for breast cancer diagnosis, due to its non-invasive, radiation-free and low-cost properties. However, the diagnostic accuracy of breast cancer is still limited due to its inherent limitations. It would be a tremendous success if we can precisely diagnose breast cancer by breast ultrasound images (BUS). Many learning-based computer-aided diagnostic methods have been proposed to achieve breast cancer diagnosis/lesion classification. However, most of them require a pre-define ROI and then classify the lesion inside the ROI. Conventional classif…
