Imaging the Human Thyroid Using Three-Dimensional Diffuse Optical Tomography: A Preliminary Study

BUViTNet: Breast Ultrasound Detection via Vision Transformers

Convolutional neural networks (CNNs) have enhanced ultrasound image-based early breast cancer detection. Vision transformers (ViTs) have recently surpassed CNNs as the most effective method for natural image analysis. ViTs have proven their capability of incorporating more global information than CNNs at lower layers, and their skip connections are more powerful than those of CNNs, which endows ViTs with superior performance. However, the effectiveness of ViTs in breast ultrasound imaging has not yet been investigated. Here, we present BUViTNet breast ultrasound detection via ViTs, where ViT-based multistage transfer learning is performed using ImageNet and cancer cell image datasets prior to transfer learning for classifying breast ultrasound images. We utilized two publicly available ultrasound breast image datasets, Mendeley and breast ultrasound images (BUSI), to train and evaluate our algorithm. The proposed method achieved the highest area under the receiver operating characteristics curve (AUC) of 1 ± 0, Matthew’s correlation coefficient (MCC) of 1 ± 0, and kappa score of 1 ± 0 on the Mendeley dataset. Furthermore, BUViTNet achieved the highest AUC of 0.968 ± 0.02, MCC of 0.961 ± 0.01, and kappa score of 0.959 ± 0.02 on the BUSI dataset. BUViTNet outperformed ViT trained from scratch, ViT-based conventional transfer learning, and CNN-based transfer learning in classifying breast ultrasound images (p < 0.01 in all cases). Our findings indicate that improved transformers are effective in analyzing breast images and can provide an improved diagnosis if used in clinical settings. Future work will consider the use of a wide range of datasets and parameters for optimized performance.

Optical diagnostic imaging and therapy for thyroid cancer

Thyroid cancer, as one of the most common endocrine cancers, has seen a surge in incidence in recent years. This is most likely due to the lack of specificity and accuracy of its traditional diagnostic modalities, leading to the overdiagnosis of thyroid nodules. Although there are several treatment options available, they are limited to surgery and ¹³¹I radiation therapy that come with significant side effects and hence cannot meet the treatment needs of anaplastic thyroid carcinoma with very high malignancy. Optical imaging that utilizes optical absorption, refraction and scattering properties, not only observes the structure and function of cells, tissues, organs, or even the whole organism to assist in diagnosis, but can also be used to perform optical therapy to achieve targeted non-invasive and precise treatment of thyroid cancer. These applications of screening, diagnosis, and treatment, lend to optical imaging's promising potential within the realm of thyroid cancer surgical navigation. Over the past decade, research on optical imaging in the diagnosis and treatment of thyroid cancer has been growing year by year, but no comprehensive review on this topic has been published. Here, we review key advances in the application of optical imaging in the diagnosis and treatment of thyroid cancer and discuss the challenges and potential for clinical translation of this technology.

Tutorial on the Use of Deep Learning in Diffuse Optical Tomography

Diffuse optical tomography using deep learning is an emerging technology that has found impressive medical diagnostic applications. However, creating an optical imaging system that uses visible and near-infrared (NIR) light is not straightforward due to photon absorption and multi-scattering by tissues. The high distortion levels caused due to these effects make the image reconstruction incredibly challenging. To overcome these challenges, various techniques have been proposed in the past, with varying success. One of the most successful techniques is the application of deep learning algorithms in diffuse optical tomography. This article discusses the current state-of-the-art diffuse optical tomography systems and comprehensively reviews the deep learning algorithms used in image reconstruction. This article attempts to provide researchers with the necessary background and tools to implement deep learning methods to solve diffuse optical tomography.