Thyroid nodule classification and segmentation in ultrasound images are crucial for computer-aided diagnosis; however, they face limitations owing to insufficient labeled data. In this study, we proposed a multi-view contrastive self-supervised method to improve thyroid nodule classification and segmentation performance with limited manual labels. Our method aligns the transverse and longitudinal views of the same nodule, thereby enabling the model to focus more on the nodule area. We designed an adaptive loss function that eliminates the limitations of the paired data. Additionally, we adopted a two-stage pre-training to exploit the pre-training on ImageNet and thyroid ultrasound images. Extensive experiments were conducted on a large-scale dataset collected from multiple centers. The results showed that the proposed method significantly improves nodule classification and segmentation performance with limited manual labels and outperforms state-of-the-art self-supervised methods. The two-stage pre-training also significantly exceeded ImageNet pre-training.
Benign prostatic hyperplasia (BPH) is a common condition, yet it is challenging for the average BPH patient to find credible and accurate information about BPH. Our goal is to evaluate and compare the accuracy and reproducibility of large language models (LLMs), including ChatGPT-3.5, ChatGPT-4, and the New Bing Chat in responding to a BPH frequently asked questions (FAQs) questionnaire.
We present a Human Artificial Intelligence Hybrid (HAIbrid) integrating framework that reweights Thyroid Imaging Reporting and Data System (TIRADS) features and the malignancy score predicted by a convolutional neural network (CNN) for nodule malignancy stratification and diagnosis. We defined extra ultrasonographical features from color Doppler images to explore malignancy-relevant features. We proposed Gated Attentional Factorization Machine (GAFM) to identify second-order interacting features trained via a 10 fold distribution-balanced stratified cross-validation scheme on ultrasound images of 3002 nodules all finally characterized by postoperative pathology (1270 malignant ones), retrospectively collected from 131 hospitals. Our GAFM-HAIbrid model demonstrated significant improvements in Area Under the Curve (AUC) value (p-value < 10−5), reaching about 0.92 over the standalone CNN (~0.87) and senior radiologists (~0.86), and identified a second-order vascularity localization and morphological pattern which was overlooked if only first-order features were considered. We validated the advantages of the integration framework on an already-trained commercial CNN system and our findings using an extra set of ultrasound images of 500 nodules. Our HAIbrid framework allows natural integration to clinical workflow for thyroid nodule malignancy risk stratification and diagnosis, and the proposed GAFM-HAIbrid model may help identify novel diagnosis-relevant second-order features beyond ultrasonography.
Breast cancer is a high incidence of malignancy in women, with a higher mortality rate. Accurate screening is helpful to early detection and improve the treatment success rate and patient survival rate. This study is based on low-cost ultrasound, using ultrasound multifeature maps based on the original radiofrequency (RF) signals and radiomics analysis method to evaluate the benign and malignant of breast tumors. The three ultrasound multifeature maps of breast tumor are composed of direct energy attenuation coefficient (AC), standard deviation of image intensity (SD) and Rician distribution parameters (RD). From the above multifeature maps, high-throughput radiomics features were extracted, then sparse representation method was used for feature selection, and then support vector machine was used to predict the benign and malignant of breast tumors. Eight groups of comparative experiments were established by using ultrasound gray-scale image, single ultrasound feature map and two ultrasound feature maps. The results from 164 patients with breast tumor showed that the AUC, accuracy and sensitivity of the radiomics classification model with feature maps of AC, SD and RD can reach 93.61%, 93.94% and 100%, respectively. The use of RF based ultrasound multifeature maps combined with radiomics could effectively predict the benign and malignant of breast tumors in this study.
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