Yu, Bing Li, Yanyan Yu, Xiangle Ai, Yao Jin, Juebin Zhang, Ji Zhang, YuHua Zhu, Hui Xie, Congying Shen, Meixiao
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Published in
Journal of digital imaging
Noninvasive differentiating thyroid follicular adenoma from carcinoma preoperatively is of great clinical value to decrease the risks resulted from excessive surgery for patients with follicular neoplasm. The purpose of this study is to investigate the accuracy of ultrasound radiomics features integrating with ultrasound features in the differentia...
Eapen, Bell Raj Kaliyadan, Feroze Ashique, Karalikkattil T
Published in
Journal of digital imaging
Clinical images are vital for diagnosing and monitoring skin diseases, and their importance has increased with the growing popularity of machine learning. Lack of standards has stifled innovation in dermatological imaging, unlike other image-intensive specialties such as radiology. We investigate the meta-requirements for utilizing the popular DICO...
Hu, Shasha Zhu, Yongbei Dong, Di Wang, Bei Zhou, Zuofu Wang, Chi Tian, Jie Peng, Yun
Published in
Journal of digital imaging
Clinical symptoms and inflammatory markers cannot reliably distinguish the etiology of CAP, and chest radiographs have abundant information related with CAP. Hence, we developed a context-fusion convolution neural network (CNN) to explore the application of chest radiographs to distinguish the etiology of CAP in children. This retrospective study i...
Pierce, Jonathan D Kosaraju, Vijaya Rosipko, Beverly Sunshine, Jeffrey L Judd, Ian Sommer, Jennifer
Published in
Journal of digital imaging
Regular communication between technologists and radiologists is necessary for maintaining optimal diagnostic image quality throughout a radiology practice. In a large hospital system with multiple sites, this task becomes increasingly difficult without simultaneously causing significant disruptions in the clinical workflow and decreased throughput....
Yin, Tang-Kai Huang, Kai-Lun Chiu, Si-Rong Yang, Yu-Qi Chang, Bao-Rong
Published in
Journal of digital imaging
To visualise the tumours inside the body on a screen, a long and thin tube is inserted with a light source and a camera at the tip to obtain video frames inside organs in endoscopy. However, multiple artefacts exist in these video frames that cause difficulty during the diagnosis of cancers. In this research, deep learning was applied to detect eig...
Nofallah, Shima Mokhtari, Mojgan Wu, Wenjun Mehta, Sachin Knezevich, Stevan May, Caitlin J Chang, Oliver H Lee, Annie C Elmore, Joann G Shapiro, Linda G
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Published in
Journal of digital imaging
The number of melanoma diagnoses has increased dramatically over the past three decades, outpacing almost all other cancers. Nearly 1 in 4 skin biopsies is of melanocytic lesions, highlighting the clinical and public health importance of correct diagnosis. Deep learning image analysis methods may improve and complement current diagnostic and progno...
Wang, Winston T Tan, Nelly Hanson, James A Crubaugh, Courtney A Hara, Amy K
Published in
Journal of digital imaging
Guidelines for COVID-19 issued by the Centers for Disease Control and Prevention prompted state and local governments to mandate safety measures for screening high-risk patient populations and for institutions to look for ways to limit human contact when possible. The aim of this study was to determine the feasibility of an automated communication ...
Xu, Xiangcong Wang, Xuehua Lin, Jingyi Xiong, Honglian Wang, Mingyi Tan, Haishu Xiong, Ke Han, Dingan
Published in
Journal of digital imaging
Automatic segmentation and measurement of the choroid layer is useful in studying of related fundus diseases, such as diabetic retinopathy and high myopia. However, most algorithms are not helpful for choroid layer segmentation due to its blurred boundaries and complex gradients. Therefore, this paper aimed to propose a novel choroid segmentation m...
Pennisi, Andrea Bloisi, Domenico D Suriani, Vincenzo Nardi, Daniele Facchiano, Antonio Giampetruzzi, Anna Rita
Published in
Journal of digital imaging
Melanoma is the deadliest form of skin cancer. Early diagnosis of malignant lesions is crucial for reducing mortality. The use of deep learning techniques on dermoscopic images can help in keeping track of the change over time in the appearance of the lesion, which is an important factor for detecting malignant lesions. In this paper, we present a ...
Parashar, Deepak Agrawal, Dheraj Kumar
Published in
Journal of digital imaging
One of the most prevalent causes of visual loss and blindness is glaucoma. Conventionally, instrument-based tools are employed for glaucoma screening. However, they are inefficient, time-consuming, and manual. Hence, computerized methodologies are needed for fast and accurate diagnosis of glaucoma. Therefore, we proposed a Computer-Aided Diagnosis ...
