Panoramic X-ray is a simple and effective tool for diagnosing dental diseases
in clinical practice. When deep learning models are developed to assist dentist
in interpreting panoramic X-rays, most of their performance suffers from the
limited annotated data, which requires dentist's expertise and a lot of time
cost. Although self-supervised learning (SSL) has been proposed to address this
challenge, the two-stage process of pretraining and fine-tuning requires even
more training time and computational resources. In this paper, we present a
self-supervised auxiliary detection (SSAD) framework, which is plug-and-play
and compatible with any detectors. It consists of a reconstruction branch and a
detection branch. Both branches are trained simultaneously, sharing the same
encoder, without the need for finetuning. The reconstruction branch learns to
restore the tooth texture of healthy or diseased teeth, while the detection
branch utilizes these learned features for diagnosis. To enhance the encoder's
ability to capture fine-grained features, we incorporate the image encoder of
SAM to construct a texture consistency (TC) loss, which extracts image
embedding from the input and output of reconstruction branch, and then enforces
both embedding into the same feature space. Extensive experiments on the public
DENTEX dataset through three detection tasks demonstrate that the proposed SSAD
framework achieves state-of-the-art performance compared to mainstream object
detection methods and SSL methods. The code is available at
this https URL

本文提出了一种自监督辅助检测 (SSAD) 框架，用于诊断牙科疾病，通过共享相同的编码器和无需微调的方式，同时训练重建分支和检测分支，以提高对细粒度特征的捕捉能力。实验证明，该框架在公共 DENTEX 数据集上的三项检测任务中取得了与主流目标检测方法和自监督学习方法相比的最先进性能。

SSAD：基于全景 X 光的牙科疾病诊断的自监督辅助检测框架

SSAD: Self-supervised Auxiliary Detection Framework for Panoramic X-ray  based Dental Disease Diagnosis

Dental diseases have a significant impact on a considerable portion of the
population, leading to various health issues that can detrimentally affect
individuals' overall well-being. The integration of automated systems in oral
healthcare has become increasingly crucial. Machine learning approaches offer a
viable solution to address challenges such as diagnostic difficulties,
inefficiencies, and errors in oral disease diagnosis. These methods prove
particularly useful when physicians struggle to predict or diagnose diseases at
their early stages. In this study, thirteen different machine learning, deep
learning, and large language models were employed to determine the severity
level of oral health issues based on radiologists' reports. The results
revealed that the Few-shot learning with SBERT and Multi-Layer Perceptron model
outperformed all other models across various experiments, achieving an
impressive accuracy of 94.1% as the best result. Consequently, this model
exhibits promise as a reliable tool for evaluating the severity of oral
diseases, enabling patients to receive more effective treatment and aiding
healthcare professionals in making informed decisions regarding resource
allocation and the management of high-risk patients.

在这项研究中，使用 13 种不同的机器学习、深度学习和大型语言模型来基于放射科医生的报告确定口腔健康问题的严重程度。结果显示，Few-shot 学习与 SBERT 和多层感知器模型在各种实验中都表现优于其他模型，取得了 94.1％的最佳准确率。因此，该模型显示出作为可靠工具评估口腔疾病严重程度的潜力，使患者能够接受更有效的治疗，并帮助医疗保健专业人员做出关于资源配置和高风险患者管理的明智决策。

通过 Few-shot 学习和 SBERT Fine-tuning 进行牙齿严重程度评估

Dental Severity Assessment through Few-shot Learning and SBERT  Fine-tuning

Detecting dental diseases through panoramic X-rays images is a standard
procedure for dentists. Normally, a dentist need to identify diseases and find
the infected teeth. While numerous machine learning models adopting this
two-step procedure have been developed, there has not been an end-to-end model
that can identify teeth and their associated diseases at the same time. To fill
the gap, we develop YOLOrtho, a unified framework for teeth enumeration and
dental disease detection. We develop our model on Dentex Challenge 2023 data,
which consists of three distinct types of annotated data. The first part is
labeled with quadrant, and the second part is labeled with quadrant and
enumeration and the third part is labeled with quadrant, enumeration and
disease. To further improve detection, we make use of Tufts Dental public
dataset. To fully utilize the data and learn both teeth detection and disease
identification simultaneously, we formulate diseases as attributes attached to
their corresponding teeth. Due to the nature of position relation in teeth
enumeration, We replace convolution layer with CoordConv in our model to
provide more position information for the model. We also adjust the model
architecture and insert one more upsampling layer in FPN in favor of large
object detection. Finally, we propose a post-process strategy for teeth layout
that corrects teeth enumeration based on linear sum assignment. Results from
experiments show that our model exceeds large Diffusion-based model.

通过全景 X 光影像来检测牙科疾病是牙医的标准程序，我们开发了 YOLOrtho，一个用于齿数目和牙科疾病检测的统一框架，通过牙齿位置关系、卷积层替换、模型架构调整、上采样和后处理策略等方法来提高检测性能。