Cross Modality Image Translation In Medical Imaging Using Generative Frameworks

Medical image-to-image (I2I) translation enables virtual scanning, i.e. the synthesis of a target imaging modality from a source one without additional acquisitions. Despite growing interest, most proposed methods operate on 2D slices, are evaluated on isolated tasks with different experimental set-ups and lack clinical validation. The primary contribution of this work is a reproducible, standardized comparative evaluation of 3D I2I translation methods in oncological imaging, designed to standardize preprocessing, splitting, inference, and multi-level evaluation across heterogeneous clinical tasks. Within this framework, we compare seven generative models, three Generative Adversarial Networks (GANs: Pix2Pix, CycleGAN, SRGAN) and four latent generative models (Latent Diffusion Model, Latent Diffusion Model+ControlNet, Brownian Bridge, Flow Matching), across eleven datasets spanning three anatomical regions (head/neck, lung, pelvis) and four translation directions (cone-beam CT to CT, MRI to CT, CT to PET, MRI T2-weighted to T2-FLAIR), for a total of 77 experiments under uniform training, inference, and evaluation conditions. The results show that GANs outperform latent generative models across all tasks, with SRGAN achieving statistically significant superiority. Our lesion-level analysis reveals that all models struggle with small lesions and that, in CT to PET synthesis, models reproduce lesion shape more reliably than absolute uptake-related intensity. We also performed a Visual Turing test administered to 17 physicians, including 15 radiologists, which shows near-chance classification accuracy (56.7%), confirming that synthetic volumes are largely indistinguishable from real acquisitions, while exposing a dissociation between quantitative metrics and clinical preference.

Written and spoken corpus of real and fake social media postings about COVID-19

This study investigates the linguistic traits of fake news and real news. There are two parts to this study: text data and speech data. The text data for this study consisted of 6420 COVID-19 related tweets re-filtered from Patwa et al. (2021). After cleaning, the dataset contained 3049 tweets, with 2161 labeled as 'real' and 888 as 'fake'. The speech data for this study was collected from TikTok, focusing on COVID-19 related videos. Research assistants fact-checked each video's content using credible sources and labeled them as 'Real', 'Fake', or 'Questionable', resulting in a dataset of 91 real entries and 109 fake entries from 200 TikTok videos with a total word count of 53,710 words. The data was analysed using the Linguistic Inquiry and Word Count (LIWC) software to detect patterns in linguistic data. The results indicate a set of linguistic features that distinguish fake news from real news in both written and speech data. This offers valuable insights into the role of language in shaping trust, social media interactions, and the propagation of fake news.