Polyp-DDPM: Diffusion-Based Semantic Polyp Synthesis for Enhanced Segmentation

This study introduces Polyp-DDPM, a diffusion-based method for generating realistic images of polyps conditioned on masks, aimed at enhancing the segmentation of gastrointestinal (GI) tract polyps. Our approach addresses the challenges of data limitations, high annotation costs, and privacy concerns associated with medical images. By conditioning the diffusion model on segmentation masks-binary masks that represent abnormal areas-Polyp-DDPM outperforms state-of-the-art methods in terms of image quality (achieving a Frechet Inception Distance (FID) score of 78.47, compared to scores above 83.79) and segmentation performance (achieving an Intersection over Union (IoU) of 0.7156, versus less than 0.6694 for synthetic images from baseline models and 0.7067 for real data). Our method generates a high-quality, diverse synthetic dataset for training, thereby enhancing polyp segmentation models to be comparable with real images and offering greater data augmentation capabilities to improve segmentation models. The source code and pretrained weights for Polyp-DDPM are made publicly available at https://github.com/mobaidoctor/polyp-ddpm.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Conditional Diffusion Models for Semantic 3D Medical Image Synthesis

This paper introduces Med-DDPM, an innovative solution using diffusion models for semantic 3D medical image synthesis, addressing the prevalent issues in medical imaging such as data scarcity, inconsistent acquisition methods, and privacy concerns. Experimental evidence illustrates that diffusion models surpass Generative Adversarial Networks (GANs) in stability and performance, generating high-quality, realistic 3D medical images. The distinct feature of Med-DDPM is its use of semantic conditioning for the diffusion model in 3D image synthesis. By controlling the generation process through pixel-level mask labels, it facilitates the creation of realistic medical images. Empirical evaluations underscore the superior performance of Med-DDPM over GAN techniques in metrics such as accuracy, stability, and versatility. Furthermore, Med-DDPM outperforms traditional augmentation techniques and synthetic GAN images in enhancing the accuracy of segmentation models. It addresses challenges such as insufficient datasets, lack of annotated data, and class imbalance. Noting the limitations of the Frechet inception distance (FID) metric, we introduce a histogram-equalized FID metric for effective performance evaluation. In summary, Med-DDPM, by utilizing diffusion models, signifies a crucial step forward in the domain of high-resolution semantic 3D medical image synthesis, transcending the limitations of GANs and data constraints. This method paves the way for a promising solution in medical imaging, primarily for data augmentation and anonymization, thus contributing significantly to the field

Women with endometriosis have higher comorbidities: Analysis of domestic data in Taiwan

AbstractEndometriosis, defined by the presence of viable extrauterine endometrial glands and stroma, can grow or bleed cyclically, and possesses characteristics including a destructive, invasive, and metastatic nature. Since endometriosis may result in pelvic inflammation, adhesion, chronic pain, and infertility, and can progress to biologically malignant tumors, it is a long-term major health issue in women of reproductive age. In this review, we analyze the Taiwan domestic research addressing associations between endometriosis and other diseases. Concerning malignant tumors, we identified four studies on the links between endometriosis and ovarian cancer, one on breast cancer, two on endometrial cancer, one on colorectal cancer, and one on other malignancies, as well as one on associations between endometriosis and irritable bowel syndrome, one on links with migraine headache, three on links with pelvic inflammatory diseases, four on links with infertility, four on links with obesity, four on links with chronic liver disease, four on links with rheumatoid arthritis, four on links with chronic renal disease, five on links with diabetes mellitus, and five on links with cardiovascular diseases (hypertension, hyperlipidemia, etc.). The data available to date support that women with endometriosis might be at risk of some chronic illnesses and certain malignancies, although we consider the evidence for some comorbidities to be of low quality, for example, the association between colon cancer and adenomyosis/endometriosis. We still believe that the risk of comorbidity might be higher in women with endometriosis than that we supposed before. More research is needed to determine whether women with endometriosis are really at risk of these comorbidities

Measuring Convexity for Figure/Ground Separation

In human perception, convex surfaces have a strong tendency to be perceived as the ”figure”. Convexity has a stronger influence on figural organization than other global shape properties, such as symmetry ([9]). And yet, there has been very little work on convexity properties in computer vision. We present a model for figure/ground segregatation which exhibits a preference for convex regions as the figure (i.e., the foreground). The model also shows a preference for smaller regions to be selected as figures, which is also known to hold for human visual perception (e.g., Koffka [11]). The model is based on the machinery of Markov random fields/random walks/diffusion processes, so that the global shape properties are obtained via local and stochastic computations. Experimental results demonstrate that our model performs well on ambiguous figure/ground displays which were not captured before. In particular, in ambiguous displays where neither region is strictly convex, the model shows preference to the “more convex ” region, thus offering a continuous measure of convexity in agreement with human perception. 1

Game bot identification based on manifold learning

In recent years, online gaming has become one of the most popular Internet activities, but cheating activity, such as the use of game bots, has increased as a consequence. Generally, the gaming community disapproves of the use of game bots, as bot users obtain unreasonable rewards without corresponding efforts. However, bots are hard to detect because they are designed to simulate human game playing behavior and they follow game rules exactly. Existing detection approaches either disrupt players ’ gaming experiences, or they assume game bots are run as standalone clients or assigned a specific goal, such as aim bots in FPS games. In this paper, we propose a manifold learning approach for detecting game bots. It is a general technique that can be applied to any game in which avatars ’ movement is controlle