Analysis of dental radiographs is an important part of the diagnostic process in daily clinical practice. Interpretation by an expert includes teeth detection and numbering. In this project, a novel solution based on adaptive histogram equalization and convolution neural network (CNN) is proposed, which automatically performs the task for dental x-rays. In order to improve the detection accuracy, we propose three pre-processing techniques to supplement the baseline CNN based on some prior domain knowledge. Firstly, image sharpening and median filtering are used to remove impulse noise, and the edge is enhanced to some extent. Next, adaptive histogram equalization is used to overcome the problem of excessive amplification noise of HE. Finally, a multi-CNN hybrid model is proposed to classify six different locations of dental slices. The results showed that the accuracy and specificity of the test set exceeded 90\%, and the AUC reached 0.97. In addition, four dentists were invited to manually annotate the test data set (independently) and then compare it with the labels obtained by our proposed algorithm. The results show that our method can effectively identify the X-ray location of teeth.
Advanced Deep Learning Techniques for Classifying Dental Conditions Using Panoramic X-Ray Images
Informatics, Oakland University, Rochester, Michigan, USA（信息学，奥克兰大学）；Department of Computer Science, Bowling Green State University, Ohio, USA（计算机科学系，布恩维尔州立大学）
In order to study the application of artificial intelligence (AI) to dental imaging, we applied AI technology to classify a set of panoramic radiographs using (a) a convolutional neural network (CNN) which is a form of an artificial neural network (ANN), (b) representative image cognition algorithms that implement scale-invariant feature transform (SIFT), and (c) histogram of oriented gradients (HOG).
ImplantMamba: Long-range Sequential Modeling Mamba For Dental Implant Position Prediction
School of Artifical Intelligence, Shenzhen University, Shenzhen, China（人工智能学院，深圳大学，中国深圳）；National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, China（大数据系统计算技术国家工程实验室，深圳大学，中国）；Huangpu People's Hospital, Zhongshan City, China（中山市黄埔人民医院，中国）；Department of Stomatology, Shenzhen University General Hospital, Shenzhen, China（口腔科，深圳大学附属医院，中国深圳）
ViT-NeBLa: A Hybrid Vision Transformer and Neural Beer-Lambert Framework for Single-View 3D Reconstruction of Oral Anatomy from Panoramic Radiographs
Artificial Intelligence and Image Processing Lab., Department of Information and Communication Engineering, Sun Moon University, South Korea（人工智能与图像处理实验室，信息与通信工程系，顺 Moon 大学，韩国）；Department of Physics and Engineering Physics, Tulane University, New Orleans, LA 70118, USA（物理系与工程物理系，路易斯安那大学，新奥尔良，路易斯安那州 70118，美国）
K-U-KAN: Koopman-Enhanced U-KAN for 3D Dental Reconstruction from a Single Panoramic X-ray Radiograph
Artificial Intelligence \& Image Processing Lab., Department of Information \& Communication Engineering, Sun Moon University, Asan-Si, South Korea；Department of Physics；Engineering Physics, Tulane University, New Orleans, LA, USA
Histo Pathology Diagnostic Center（组织病理诊断中心）；Technical University of Munich（慕尼黑工业大学）
We present a summary of the domain adaptive cascade R-CNN method for mitosis detection of digital histopathology images. By comprehensive data augmentation and adapting existing popular detection architecture, our proposed method has achieved an F1 score of 0.7500 on the preliminary test set in MItosis DOmain Generalization (MIDOG) Challenge at MICCAI 2021.
Fundamental to improving Dental and Orthodontic treatments is the ability to quantitatively assess and cross-compare their outcomes. Such assessments require calculating distances and angles from 3D coordinates of dental landmarks. The costly and repetitive task of hand-labelling dental models impedes studies requiring large sample size to penetrate statistical noise. We have developed techniques and software implementing these techniques to map out automatically, 3D dental scans. This process is divided into consecutive steps - determining a model's orientation, separating and identifying the…
Faculty of Sciences（理学院）；Mohammed V University（穆罕默德五世大学）；École Normale Supérieure（高等师范学院）
Cervical cancer will cause 460 000 deaths per year by 2040, approximately 90% are Sub-Saharan African women. A constantly increasing incidence in Africa making cervical cancer a priority by the World Health Organization (WHO) in terms of screening, diagnosis, and treatment. Conventionally, cancer diagnosis relies primarily on histopathological assessment, a deeply error-prone procedure requiring intelligent computer-aided systems as low-cost patient safety mechanisms but lack of labeled data in digital pathology limits their applicability. In this study, few cervical tissue digital slides from…
Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院）；Siemens Healthineers（西门子医疗）
Purpose: To leverage volumetric quantification of airspace disease (AD) derived from a superior modality (CT) serving as ground truth, projected onto digitally reconstructed radiographs (DRRs) to: 1) train a convolutional neural network to quantify airspace disease on paired CXRs; and 2) compare the DRR-trained CNN to expert human readers in the CXR evaluation of patients with confirmed COVID-19. Materials and Methods: We retrospectively selected a cohort of 86 COVID-19 patients (with positive RT-PCR), from March-May 2020 at a tertiary hospital in the northeastern USA, who underwent chest CT a…
We propose a new model for digital pathology segmentation, based on the observation that histopathology images are inherently symmetric under rotation and reflection. Utilizing recent findings on rotation equivariant CNNs, the proposed model leverages these symmetries in a principled manner. We present a visual analysis showing improved stability on predictions, and demonstrate that exploiting rotation equivariance significantly improves tumor detection performance on a challenging lymph node metastases dataset. We further present a novel derived dataset to enable principled comparison of mach…
The Norwegian University of Science and Technology（挪威科技大学）；St. Olavs Hospital, Trondheim University Hospital（圣奥拉夫斯医院，特隆赫姆大学医院）；SINTEF
Deep convolutional neural networks (CNNs) are the current state-of-the-art for digital analysis of histopathological images. The large size of whole-slide microscopy images (WSIs) requires advanced memory handling to read, display and process these images. There are several open-source platforms for working with WSIs, but few support deployment of CNN models. These applications use third-party solutions for inference, making them less user-friendly and unsuitable for high-performance image analysis. To make deployment of CNNs user-friendly and feasible on low-end machines, we have developed a …
F. Hoffmann-La Roche AG（罗氏制药）；Genentech, Inc.（基因泰克公司）
Recently, vision transformers were shown to be capable of outperforming convolutional neural networks when pretrained on sufficient amounts of data. In comparison to convolutional neural networks, vision transformers have a weaker inductive bias and therefore allow a more flexible feature detection. Due to their promising feature detection, this work explores vision transformers for tumor detection in digital pathology whole slide images in four tissue types, and for tissue type identification. We compared the patch-wise classification performance of the vision transformer DeiT-Tiny to the sta…
University of Waterloo（滑铁卢大学）；University of Toronto（多伦多大学）；Huron Digital Pathology（休伦数字病理公司）
Out-of-focus microscopy lens in digital pathology is a critical bottleneck in high-throughput Whole Slide Image (WSI) scanning platforms, for which pixel-level automated Focus Quality Assessment (FQA) methods are highly desirable to help significantly accelerate the clinical workflows. Existing FQA methods include both knowledge-driven and data-driven approaches. While data-driven approaches such as Convolutional Neural Network (CNN) based methods have shown great promises, they are difficult to use in practice due to their high computational complexity and lack of transferability. Here, we pr…
Tel Aviv University（特拉维夫大学）；The Chaim Sheba Medical Center（查姆·谢巴医疗中心）；Rabin Medical Center（拉宾医疗中心）
Stroke is the second most frequent cause of death worldwide with a considerable economic burden on the health systems. In about 15% of strokes, atherosclerotic carotid plaques (ACPs) constitute the main etiological factor. Early detection of ACPs may have a key-role for preventing strokes by managing the patient a-priory to the occurrence of the damage. ACPs can be detected on panoramic images. As these are one of the most common images performed for routine dental practice, they can be used as a source of available data for computerized methods of automatic detection in order to significantly…
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…
In this paper, we present a deep learning-based image processing technique for extraction of bone structures in chest radiographs using a U-Net FCNN. The U-Net was trained to accomplish the task in a fully supervised setting. To create the training image pairs, we employed simulated X-Ray or Digitally Reconstructed Radiographs (DRR), derived from 664 CT scans belonging to the LIDC-IDRI dataset. Using HU based segmentation of bone structures in the CT domain, a synthetic 2D "Bone x-ray" DRR is produced and used for training the network. For the reconstruction loss, we utilize two loss functions…
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）
University of British Columbia（不列颠哥伦比亚大学）；Siemens Healthcare（西门子医疗）
In this paper, we present a Convolutional Neural Network (CNN) regression approach for real-time 2-D/3-D registration. Different from optimization-based methods, which iteratively optimize the transformation parameters over a scalar-valued metric function representing the quality of the registration, the proposed method exploits the information embedded in the appearances of the Digitally Reconstructed Radiograph and X-ray images, and employs CNN regressors to directly estimate the transformation parameters. The CNN regressors are trained for local zones and applied in a hierarchical manner to…
Kharkevich Institute for Information Transmission Problems（信息传输问题研究所（Kharkevich研究所））；Moscow Institute of Physics and Technology（莫斯科物理技术学院）；Skolkovo Institute of Science and Technology（斯科尔科沃科学技术研究院）
In the last years, neural networks have proven to be a powerful framework for various image analysis problems. However, some application domains have specific limitations. Notably, digital pathology is an example of such fields due to tremendous image sizes and quite limited number of training examples available. In this paper, we adopt state-of-the-art convolutional neural networks (CNN) architectures for digital pathology images analysis. We propose to classify image patches to increase effective sample size and then to apply an ensembling technique to build prediction for the original image…
Sunnybrook Research Institute（森尼布鲁克研究所）；University of Toronto（多伦多大学）；Sunnybrook Health Sciences Centre（森尼布鲁克健康科学中心）
Digital pathology has advanced substantially over the last decade however tumor localization continues to be a challenging problem due to highly complex patterns and textures in the underlying tissue bed. The use of convolutional neural networks (CNNs) to analyze such complex images has been well adopted in digital pathology. However in recent years, the architecture of CNNs have altered with the introduction of inception modules which have shown great promise for classification tasks. In this paper, we propose a modified "transition" module which learns global average pooling layers from filt…
Princeton University（普林斯顿大学）；The Chinese University of Hong Kong（香港中文大学）；Microsoft（微软）；UCLA（加州大学洛杉矶分校）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；Microsoft Research New England（微软新英格兰研究院）；VSCO
We propose DeepMiner, a framework to discover interpretable representations in deep neural networks and to build explanations for medical predictions. By probing convolutional neural networks (CNNs) trained to classify cancer in mammograms, we show that many individual units in the final convolutional layer of a CNN respond strongly to diseased tissue concepts specified by the BI-RADS lexicon. After expert annotation of the interpretable units, our proposed method is able to generate explanations for CNN mammogram classification that are consistent with ground truth radiology reports on the Di…
Tampere University（坦佩雷大学）；Izmir University of Economics（伊兹密尔经济大学）；Qatar University（卡塔尔大学）；Eskişehir Osmangazi University（埃斯基谢希尔奥斯曼加齐大学）
Glaucoma leads to permanent vision disability by damaging the optical nerve that transmits visual images to the brain. The fact that glaucoma does not show any symptoms as it progresses and cannot be stopped at the later stages, makes it critical to be diagnosed in its early stages. Although various deep learning models have been applied for detecting glaucoma from digital fundus images, due to the scarcity of labeled data, their generalization performance was limited along with high computational complexity and special hardware requirements. In this study, compact Self-Organized Operational N…
FEMTO-ST Institute, CNRS Univ. Bourgogne Franche-Comte (UBFC)（法国国家科学研究中心勃艮第弗朗什-孔泰大学FEMTO-ST研究所）
In Digital Holography (DH), it is crucial to extract the object distance from a hologram in order to reconstruct its amplitude and phase. This step is called auto-focusing and it is conventionally solved by first reconstructing a stack of images and then by sharpening each reconstructed image using a focus metric such as entropy or variance. The distance corresponding to the sharpest image is considered the focal position. This approach, while effective, is computationally demanding and time-consuming. In this paper, the determination of the distance is performed by Deep Learning (DL). Two dee…
Digital holography enables us to reconstruct objects in three-dimensional space from holograms captured by an imaging device. For the reconstruction, we need to know the depth position of the recoded object in advance. In this study, we propose depth prediction using convolutional neural network (CNN)-based regression. In the previous researches, the depth of an object was estimated through reconstructed images at different depth positions from a hologram using a certain metric that indicates the most focused depth position; however, such a depth search is time-consuming. The CNN of the propos…
Automated methods for breast cancer detection have focused on 2D mammography and have largely ignored 3D digital breast tomosynthesis (DBT), which is frequently used in clinical practice. The two key challenges in developing automated methods for DBT classification are handling the variable number of slices and retaining slice-to-slice changes. We propose a novel deep 2D convolutional neural network (CNN) architecture for DBT classification that simultaneously overcomes both challenges. Our approach operates on the full volume, regardless of the number of slices, and allows the use of pre-trai…
Convolutional Neural Networks (CNNs) constitute a class of Deep Learning models which have been used in the recent past to resolve many problems in computer vision, in particular optical flow estimation. Measuring displacement and strain fields can be regarded as a particular case of this problem. However, it seems that CNNs have never been used so far to perform such measurements. This work is aimed at implementing a CNN able to retrieve displacement and strain fields from pairs of reference and deformed images of a flat speckled surface, as Digital Image Correlation (DIC) does. This paper ex…
University of Kentucky（肯塔基大学）；Miami University（迈阿密大学）
Breast cancer is the malignant tumor that causes the highest number of cancer deaths in females. Digital mammograms (DM or 2D mammogram) and digital breast tomosynthesis (DBT or 3D mammogram) are the two types of mammography imagery that are used in clinical practice for breast cancer detection and diagnosis. Radiologists usually read both imaging modalities in combination; however, existing computer-aided diagnosis tools are designed using only one imaging modality. Inspired by clinical practice, we propose an innovative convolutional neural network (CNN) architecture for breast cancer classi…
Digital image watermarking has been widely used in different applications such as copyright protection of digital media, such as audio, image, and video files. Two opposing criteria of robustness and transparency are the goals of watermarking methods. In this paper, we propose a framework for determining the appropriate embedding strength factor. The framework can use most DWT and DCT based blind watermarking approaches. We use Mask R-CNN on the COCO dataset to find a good strength factor for each sub-block. Experiments show that this method is robust against different attacks and has good tra…
Department of Information Engineering, University of Siena（锡耶纳大学信息工程系）；University of Siena（锡耶纳大学）
Last-generation GAN models allow to generate synthetic images which are visually indistinguishable from natural ones, raising the need to develop tools to distinguish fake and natural images thus contributing to preserve the trustworthiness of digital images. While modern GAN models can generate very high-quality images with no visible spatial artifacts, reconstruction of consistent relationships among colour channels is expectedly more difficult. In this paper, we propose a method for distinguishing GAN-generated from natural images by exploiting inconsistencies among spectral bands, with spe…
University of Warwick（华威大学）；National University of Sciences and Technology（国立科技大学）；University Hospitals Coventry（考文垂大学医院）；The Alan Turing Institute（艾伦·图灵研究所）
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 fr…
