A Novel Hybrid Deep Learning and Chaotic Dynamics Approach for Thyroid Cancer Classification
Laboratory of Renewable Energy, Department of Electronics, University of Jijel（可再生能源实验室，电子系，杰吉尔大学）；Non-Destructive Testing Laboratory, Department of Electronics, University of Jijel（无损检测实验室，电子系，杰吉尔大学）；Institute of Technology, University Center Salhi Ahmed（技术学院，Salhi Ahmed大学中心）；College of Engineering and Information Technology, University of Dubai（工程与信息科技学院，迪拜大学）
Timely and accurate diagnosis is crucial in addressing the global rise in thyroid cancer, ensuring effective treatment strategies and improved patient outcomes. We present an intelligent classification method that couples an Adaptive Convolutional Neural Network (CNN) with Cohen-Daubechies-Feauveau (CDF9/7) wavelets whose detail coefficients are modulated by an n-scroll chaotic system to enrich discriminative features. We evaluate on the public DDTI thyroid ultrasound dataset (n = 1,638 images; 819 malignant / 819 benign) using 5-fold cross-validation, where the proposed method attains 98.17% accuracy, 98.76% sensitivity, 97.58% specificity, 97.55% F1-score, and an AUC of 0.9912. A controlled ablation shows that adding chaotic modulation to CDF9/7 improves accuracy by +8.79 percentage points over a CDF9/7-only CNN (from 89.38% to 98.17%). To objectively position our approach, we trained state-of-the-art backbones on the same data and splits: EfficientNetV2-S (96.58% accuracy; AUC 0.987), Swin-T (96.41%; 0.986), ViT-B/16 (95.72%; 0.983), and ConvNeXt-T (96.94%; 0.987). Our method outperforms the best of these by +1.23 points in accuracy and +0.0042 in AUC, while remaining computationally efficient (28.7 ms per image; 1,125 MB peak VRAM). Robustness is further supported by cross-dataset testing on TCIA (accuracy 95.82%) and transfer to an ISIC skin-lesion subset (n = 28 unique images, augmented to 2,048; accuracy 97.31%). Explainability analyses (Grad-CAM, SHAP, LIME) highlight clinically relevant regions. Altogether, the wavelet-chaos-CNN pipeline delivers state-of-the-art thyroid ultrasound classification with strong generalization and practical runtime characteristics suitable for clinical integration.
Thyroid cancer is common worldwide, with a rapid increase in prevalence across North America in recent years. While most patients present with palpable nodules through physical examination, a large number of small and medium-sized nodules are detected by ultrasound examination. Suspicious nodules are then sent for biopsy through fine needle aspiration. Since biopsies are invasive and sometimes inconclusive, various research groups have tried to develop computer-aided diagnosis systems. Earlier approaches along these lines relied on clinically relevant features that were manually identified by …
Duke University（杜克大学）；Duke University Medical Center（杜克大学医学中心）
We consider preoperative prediction of thyroid cancer based on ultra-high-resolution whole-slide cytopathology images. Inspired by how human experts perform diagnosis, our approach first identifies and classifies diagnostic image regions containing informative thyroid cells, which only comprise a tiny fraction of the entire image. These local estimates are then aggregated into a single prediction of thyroid malignancy. Several unique characteristics of thyroid cytopathology guide our deep-learning-based approach. While our method is closely related to multiple-instance learning, it deviates fr…
An Approach for Thyroid Nodule Analysis Using Thermographic Images
Computer Science Department, Universidade Federal Fluminense（联邦弗里蒙特大学计算机科学系）；Radiology Department, Hospital Universitário Antônio Pedro (HUAP)（安东尼奥佩德罗大学医院放射科）；Applied Computation Group NCA-UFMA, Universidade Federal do Maranhão（马兰舍大学应用计算组NCA-UFMA）
Over the past decades, the incidence of thyroid cancer has been increasing globally. Accurate and early diagnosis allows timely treatment and helps to avoid over-diagnosis. Clinically, a nodule is commonly evaluated from both transverse and longitudinal views using thyroid ultrasound. However, the appearance of the thyroid gland and lesions can vary dramatically across individuals. Identifying key diagnostic information from both views requires specialized expertise. Furthermore, finding an optimal way to integrate multi-view information also relies on the experience of clinicians and adds fur…
MixUp-MIL: Novel Data Augmentation for Multiple Instance Learning and a Study on Thyroid Cancer Diagnosis
Salzburg University of Applied Sciences（萨尔茨堡应用科学大学）；Spinal Cord Injury and Tissue Regeneration Center Salzburg（萨尔茨堡脊髓损伤与组织再生中心）；Research Institute of Experimental Neuroregeneration（实验神经再生研究所）；Kardinal Schwarzenberg Klinikum（红衣主教施瓦岑贝格医院）
Evaluation of Multi-Scale Multiple Instance Learning to Improve Thyroid Cancer Classification
Salzburg University of Applied Sciences（萨尔茨堡应用科技大学）；Spinal Cord Injury and Tissue Regeneration Center Salzburg（萨尔茨堡脊髓损伤与组织再生中心）；Research Institute of Experimental Neuroregeneration（实验神经再生研究所）；Kardinal Schwarzenberg Klinikum（卡迪纳尔·施瓦岑贝格医院）；University of Salzburg（萨尔茨堡大学）
College of Electronic and Information Engineering of Hebei University（河北大学电子信息工程学院）
The incidence of thyroid nodule is very high and generally increases with the age. Thyroid nodule may presage the emergence of thyroid cancer. The thyroid nodule can be completely cured if detected early. Fine needle aspiration cytology is a recognized early diagnosis method of thyroid nodule. There are still some limitations in the fine needle aspiration cytology, and the ultrasound diagnosis of thyroid nodule has become the first choice for auxiliary examination of thyroid nodular disease. If we could combine medical imaging technology and fine needle aspiration cytology, the diagnostic rate…
Monash University（莫纳什大学）；Alfred Hospital（阿尔弗雷德医院）；West China Hospital of Sichuan University（四川大学华西医院）
Thyroid disease instances have been continuously increasing since the 1990s, and thyroid cancer has become the most rapidly rising disease among all the malignancies in recent years. Most existing studies focused on applying deep convolutional neural networks for detecting thyroid cancer. Despite their satisfactory performance on binary classification tasks, limited studies have explored multi-class classification of thyroid disease types; much less is known of the diagnosis of co-existence situation for different types of thyroid diseases. Therefore, this study proposed a novel multi-channel …
Nodule-DETR: A Novel DETR Architecture with Frequency-Channel Attention for Ultrasound Thyroid Nodule Detection
Beihang University（北京航空航天大学）；National Cancer Center（国家癌症中心）；National Clinical Research Center for Cancer（国家癌症临床医学研究中心）；Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（中国医学科学院北京协和医学院肿瘤医院）；Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University（北京航空航天大学生物医学工程高精尖创新中心）
STACT-Time: Spatio-Temporal Cross Attention for Cine Thyroid Ultrasound Time Series Classification
Medical \& Imaging Informatics, Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles (UCLA), CA, USA Department of Bioengineering, UCLA, Los Angeles, CA, USA Department of Electrical；Computer Engineering, UCLA, Los Angeles, CA, USA Department of Radiological Sciences, UCLA, Los Angeles, CA, USA
TAM-Chain: Multi-Scale Thyroid Cytology Classification via Absorbing Markov Chains and Shannon Entropy Uncertainty Quantification for False-Negative Suppression and Domain-Shift Adaptation
Faculty of Mathematics, Mechanics and Informatics, VNU University of Science（越南国立大学理学院数学、力学与信息学系）
Intelligent Diagnosis Using Dual-Branch Attention Network for Rare Thyroid Carcinoma Recognition with Ultrasound Imaging
Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University（上海多维信息处理重点实验室，华东师范大学）；School of Mathematics and Physics, Xi'an Jiaotong-Liverpool University（西安交通大学利物浦大学数学与物理学院）；Department of Ultrasound, Shanghai Tenth People's Hospital（上海第十人民医院超声科）；Shanghai Engineering Research Center of Ultrasound Diagnosis and Treatment, Tongji University School of Medicine（上海超声诊断与治疗工程研究中心，同济大学医学院）；Department of Mathematical Sciences, University of Liverpool（利物浦大学数学科学系）
Benchmarking Deep Learning Models for Laryngeal Cancer Staging Using the LaryngealCT Dataset
School of Information Technology, Deakin University（德肯大学信息科技学院）；Department of Head and Neck Surgery, Kasturba Medical College, Manipal Academy of Higher Education（曼谷医学院头颈外科部门，曼谷高等教育学院）；Department of Radiodiagnosis and Imaging, Kasturba Medical College, Manipal Academy of Higher Education（曼谷医学院放射诊断与影像部门，曼谷高等教育学院）；Manipal Institute of Technology, Manipal Academy of Higher Education（曼谷技术学院，曼谷高等教育学院）
Institute Mihajlo Pupin, University of Belgrade（贝尔格莱德大学米哈伊洛·普平研究所）；Center for Pathology and Forensics of the Military Medical Academy in Belgrade（贝尔格莱德军事医学科学院病理与法医中心）；University of Siegen（锡根大学）；Wireill；The School of Computing, University Union AB（AB大学联盟计算机学院）
This paper shows results of computer analysis of images in the purpose of finding differences between medical images in order of their classifications in terms of separation malign tissue from a normal and benign tissue. The diagnostics of malign tissue is of the crucial importance in medicine. Therefore, ascertainment of the correlation between multifractals parameters and "chaotic" cells could be of the great appliance. This paper shows the application of multifractal analysis for additional help in cancer diagnosis, as well as diminishing. of the subjective factor and error probability
Eindhoven University of Technology（埃因霍温理工大学）；University of Amsterdam（阿姆斯特丹大学）；University Hospital Hamburg-Eppendorf（汉堡-埃彭多夫大学医院）；Universidade de São Paulo Faculdade de Medicina Hospital das Clínicas（圣保罗大学医学院临床医院）
Shear-wave elastography (SWE) permits local estimation of tissue elasticity, an important imaging marker in biomedicine. This recently-developed, advanced technique assesses the speed of a laterally-travelling shear wave after an acoustic radiation force "push" to estimate local Young's moduli in an operator-independent fashion. In this work, we show how synthetic SWE (sSWE) images can be generated based on conventional B-mode imaging through deep learning. Using side-by-side-view B-mode/SWE images collected in 50 patients with prostate cancer, we show that sSWE images with a pixel-wise mean a…
