Diffusion-Based Hierarchical Multi-Label Object Detection to Analyze Panoramic Dental X-rays

Due to the necessity for precise treatment planning, the use of panoramic X-rays to identify different dental diseases has tremendously increased. Although numerous ML models have been developed for the interpretation of panoramic X-rays, there has not been an end-to-end model developed that can identify problematic teeth with dental enumeration and associated diagnoses at the same time. To develop such a model, we structure the three distinct types of annotated data hierarchically following the FDI system, the first labeled with only quadrant, the second labeled with quadrant-enumeration, and the third fully labeled with quadrant-enumeration-diagnosis. To learn from all three hierarchies jointly, we introduce a novel diffusion-based hierarchical multi-label object detection framework by adapting a diffusion-based method that formulates object detection as a denoising diffusion process from noisy boxes to object boxes. Specifically, to take advantage of the hierarchically annotated data, our method utilizes a novel noisy box manipulation technique by adapting the denoising process in the diffusion network with the inference from the previously trained model in hierarchical order. We also utilize a multi-label object detection method to learn efficiently from partial annotations and to give all the needed information about each abnormal tooth for treatment planning. Experimental results show that our method significantly outperforms state-of-the-art object detection methods, including RetinaNet, Faster R-CNN, DETR, and DiffusionDet for the analysis of panoramic X-rays, demonstrating the great potential of our method for hierarchically and partially annotated datasets. The code and the data are available at: https://github.com/ibrahimethemhamamci/HierarchicalDet.Comment: MICCAI 202

DENTEX: An Abnormal Tooth Detection with Dental Enumeration and Diagnosis Benchmark for Panoramic X-rays

Panoramic X-rays are frequently used in dentistry for treatment planning, but their interpretation can be both time-consuming and prone to error. Artificial intelligence (AI) has the potential to aid in the analysis of these X-rays, thereby improving the accuracy of dental diagnoses and treatment plans. Nevertheless, designing automated algorithms for this purpose poses significant challenges, mainly due to the scarcity of annotated data and variations in anatomical structure. To address these issues, the Dental Enumeration and Diagnosis on Panoramic X-rays Challenge (DENTEX) has been organized in association with the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) in 2023. This challenge aims to promote the development of algorithms for multi-label detection of abnormal teeth, using three types of hierarchically annotated data: partially annotated quadrant data, partially annotated quadrant-enumeration data, and fully annotated quadrant-enumeration-diagnosis data, inclusive of four different diagnoses. In this paper, we present the results of evaluating participant algorithms on the fully annotated data, additionally investigating performance variation for quadrant, enumeration, and diagnosis labels in the detection of abnormal teeth. The provision of this annotated dataset, alongside the results of this challenge, may lay the groundwork for the creation of AI-powered tools that can offer more precise and efficient diagnosis and treatment planning in the field of dentistry. The evaluation code and datasets can be accessed at https://github.com/ibrahimethemhamamci/DENTEXComment: MICCAI 2023 Challeng

GaNDLF: A Generally Nuanced Deep Learning Framework for Scalable End-to-End Clinical Workflows in Medical Imaging

Deep Learning (DL) has greatly highlighted the potential impact of optimized machine learning in both the scientific and clinical communities. The advent of open-source DL libraries from major industrial entities, such as TensorFlow (Google), PyTorch (Facebook), and MXNet (Apache), further contributes to DL promises on the democratization of computational analytics. However, increased technical and specialized background is required to develop DL algorithms, and the variability of implementation details hinders their reproducibility. Towards lowering the barrier and making the mechanism of DL development, training, and inference more stable, reproducible, and scalable, without requiring an extensive technical background, this manuscript proposes the Generally Nuanced Deep Learning Framework (GaNDLF). With built-in support for k-fold cross-validation, data augmentation, multiple modalities and output classes, and multi-GPU training, as well as the ability to work with both radiographic and histologic imaging, GaNDLF aims to provide an end-to-end solution for all DL-related tasks, to tackle problems in medical imaging and provide a robust application framework for deployment in clinical workflows

QCD and strongly coupled gauge theories : challenges and perspectives

We highlight the progress, current status, and open challenges of QCD-driven physics, in theory and in experiment. We discuss how the strong interaction is intimately connected to a broad sweep of physical problems, in settings ranging from astrophysics and cosmology to strongly coupled, complex systems in particle and condensed-matter physics, as well as to searches for physics beyond the Standard Model. We also discuss how success in describing the strong interaction impacts other fields, and, in turn, how such subjects can impact studies of the strong interaction. In the course of the work we offer a perspective on the many research streams which flow into and out of QCD, as well as a vision for future developments.Peer reviewe

Two phases of macrophages: Inducing maturation and death of oligodendrocytes in vitro co-culture

Background: The plasticity of macrophages in the immune response is a dynamic situation dependent on external stimuli. The activation of macrophages both has beneficial and detrimental effects on mature oligodendrocytes (OLs) and myelin. The activation towards inflammatory macrophages has a critical role in the immune-mediated oligodendrocytes death in multiple sclerosis (MS) lesions. New method: We established an in vitro co-culture method to study the function of macrophages in the survival and maturation of OLs. Results: We revealed that M1 macrophages decreased the number of mature OLs and phagocytosed the myelin. Interestingly, non-activated as well as M2 macrophages contributed to an increase in the number of mature OLs in our in vitro co-culture platform. Comparison with existing methods: We added an antibody against an OL surface antigen in our in vitro co-cultures. The antibody presents the OLs to the macrophages enabling the investigation of direct interactions between macrophages and OLs. Conclusion: Our co-culture system is a feasible method for the investigation of the direct cell-to-cell interactions between OLs and macrophages. We utilized it to show that M2 and non-activated macrophages may be employed to enhance remyelination

Magnetic cobalt particle-assisted solid phase extraction of tellurium prior to its determination by slotted quartz tube-flame atomic absorption spectrophotometry

WOS: 000466901800005PubMed: 31053958The emergence of magnetic materials has opened up doors to numerous applications including their use as sorbents for preconcentration of trace elements. Magnetic materials exhibit many unique advantages in sample preparation such as easy separation from the sample, high preconcentration factor, and short operation period. In the present study, magnetic cobalt material was synthesized, characterized, and used as an effective sorbent in a solid phase extraction process. Experimental variables of the extraction process including pH and volume of buffer solution, eluent concentration and volume, mixing type and period, and sorbent amount were optimized to achieve maximum extraction efficiency. Instrumental variables of flame atomic absorption spectrophotometry and the type of slotted quartz tube were also investigated. Under the optimum conditions, the combined method provided a wide linear range between 50 and 200ng/mL with detection and quantification limits of 15.4ng/mL and 51.3ng/mL, respectively. Relative standard deviations of the proposed method were less than 5.0% and a high enrichment factor of 86.7 was obtained. The proposed method was successfully applied to soil samples for the determination of trace tellurium

Diffusion-based hierarchical multi-label object detection to analyze panoramic dental X-rays

Due to the necessity for precise treatment planning, the use of panoramic X-rays to identify different dental diseases has tremendously increased. Although numerous ML models have been developed for the interpretation of panoramic X-rays, there has not been an end-to-end model developed that can identify problematic teeth with dental enumeration and associated diagnoses at the same time. To develop such a model, we structure the three distinct types of annotated data hierarchically following the FDI system, the first labeled with only quadrant, the second labeled with quadrant-enumeration, and the third fully labeled with quadrant-enumeration-diagnosis. To learn from all three hierarchies jointly, we introduce a novel diffusion-based hierarchical multi-label object detection framework by adapting a diffusion-based method that formulates object detection as a denoising diffusion process from noisy boxes to object boxes. Specifically, to take advantage of the hierarchically annotated data, our method utilizes a novel noisy box manipulation technique by adapting the denoising process in the diffusion network with the inference from the previously trained model in hierarchical order. We also utilize a multi-label object detection method to learn efficiently from partial annotations and to give all the needed information about each abnormal tooth for treatment planning. Experimental results show that our method significantly outperforms state-of-the-art object detection methods, including RetinaNet, Faster R-CNN, DETR, and DiffusionDet for the analysis of panoramic X-rays, demonstrating the great potential of our method for hierarchically and partially annotated datasets. The code and the datasets are available at https://github.com/ibrahimethemhamamci/HierarchicalDet.Helmut Horten Stiftun