Mask-conditioned latent diffusion for generating gastrointestinal polyp images

In order to take advantage of AI solutions in endoscopy diagnostics, we must overcome the issue of limited annotations. These limitations are caused by the high privacy concerns in the medical field and the requirement of getting aid from experts for the time-consuming and costly medical data annotation process. In computer vision, image synthesis has made a significant contribution in recent years as a result of the progress of generative adversarial networks (GANs) and diffusion probabilistic models (DPM). Novel DPMs have outperformed GANs in text, image, and video generation tasks. Therefore, this study proposes a conditional DPM framework to generate synthetic GI polyp images conditioned on given generated segmentation masks. Our experimental results show that our system can generate an unlimited number of high-fidelity synthetic polyp images with the corresponding ground truth masks of polyps. To test the usefulness of the generated data, we trained binary image segmentation models to study the effect of using synthetic data. Results show that the best micro-imagewise IOU of 0.7751 was achieved from DeepLabv3+ when the training data consists of both real data and synthetic data. However, the results reflect that achieving good segmentation performance with synthetic data heavily depends on model architectures

Osteopoikilosis: a rare case with interesting imaging

Background: Osteopoikilosis (OPK) is a rare osteosclerotic dysplasia. It is usually asymptomatic and diagnosis is made incidentally by radiographic findings. It has a unique radiographic presentation with multiple small, well-defined, circular, or ovoid radiodensities which are distributed symmetrically in the epiphysis and metaphysis of long bones.Aim of the work: In this case report, a 38-year-old man with mild joint discomfort was diagnosed with OPK according to his radiographic findings and literature review.Conclusion: It is important to diagnose OPK and to distinguish it from other medical conditions to calm the patient and to reduce unnecessary investigation

Survey on ethnobotanical uses of anti-cancer herbs in Southern region of Ilam, West Iran

One of the most common problems in the medical world is the resistance of cancer cells to anti-tumor drugs, so finding new anticancer agents with minimal side effect is essential. This study aims at identifying medicinal plants in to the southern region of Ilam province in west Iran, which are traditionally used in the treatment of cancer by herbal practitioners. This study was conducted in the southern district of Ilam province, Iran. The study was conducted from August 2013 to October 2014 by using questionnaire and interview from herbal practitioners. The collected data were analyzed through relative frequency of citation index (RFC). In this study, 36 herbal practitioners were interviewed. A sum of 21 medicinal plants used in variety of cancers from16 families were identified for the Southern District of Ilam. Asteraceae was the dominant plant family, and the most used organ was aerial parts (44%). Dermal cancer was the most treated cancer by herbal practitioners in the region with different herbs. Lawsonia inermis and Satureja khuzistanica were the most cited species for anticancer use. On comparison with the literature it was revealed that 61.9% plants are not scientifically validated against any type of cancer. New therapeutic remedies were reported for the first time and a number of similar effects of the reported plants were found in other studies. As a result of the present study we recommend the plants documented in the present study, which are not pharmacologically assessed, for further pharmacological studies

Association of SMAD7 genetic markers and haplotypes with colorectal cancer risk

Funding Information: This work was based on the Master of Science thesis of Ms. Maryam Alidoust and was financially supported by Mashhad University of Medical Sciences (Grant No: 951659).Peer reviewedPublisher PD