ColonGen: an efficient polyp segmentation system for generalization improvement using a new comprehensive dataset.

Colorectal cancer (CRC) is one of the most common causes of cancer-related deaths. While polyp detection is important for diagnosing CRC, high miss rates for polyps have been reported during colonoscopy. Most deep learning methods extract features from images using convolutional neural networks (CNNs). In recent years, vision transformer (ViT) models have been employed for image processing and have been successful in image segmentation. It is possible to improve image processing by using transformer models that can extract spatial location information, and CNNs that are capable of aggregating local information. Despite this, recent research shows limited effectiveness in increasing data diversity and generalization accuracy. This paper investigates the generalization proficiency of polyp image segmentation based on transformer architecture and proposes a novel approach using two different ViT architectures. This allows the model to learn representations from different perspectives, which can then be combined to create a richer feature representation. Additionally, a more universal and comprehensive dataset has been derived from the datasets presented in the related research, which can be used for improving generalizations. We first evaluated the generalization of our proposed model using three distinct training-testing scenarios. Our experimental results demonstrate that our ColonGen-V1 outperforms other state-of-the-art methods in all scenarios. As a next step, we used the comprehensive dataset for improving the performance of the model against in- and out-of-domain data. The results show that our ColonGen-V2 outperforms state-of-the-art studies by 5.1%, 1.3%, and 1.1% in ETIS-Larib, Kvasir-Seg, and CVC-ColonDB datasets, respectively. The inclusive dataset and the model introduced in this paper are available to the public through this link: https://github.com/javadmozaffari/Polyp_segmentation .

A survey on deep learning models for detection of COVID-19.

The spread of the COVID-19 started back in 2019; and so far, more than 4 million people around the world have lost their lives to this deadly virus and its variants. In view of the high transmissibility of the Corona virus, which has turned this disease into a global pandemic, artificial intelligence can be employed as an effective tool for an earlier detection and treatment of this illness. In this review paper, we evaluate the performance of the deep learning models in processing the X-Ray and CT-Scan images of the Corona patients' lungs and describe the changes made to these models in order to enhance their Corona detection accuracy. To this end, we introduce the famous deep learning models such as VGGNet, GoogleNet and ResNet and after reviewing the research works in which these models have been used for the detection of COVID-19, we compare the performances of the newer models such as DenseNet, CapsNet, MobileNet and EfficientNet. We then present the deep learning techniques of GAN, transfer learning, and data augmentation and examine the statistics of using these techniques. Here, we also describe the datasets introduced since the onset of the COVID-19. These datasets contain the lung images of Corona patients, healthy individuals, and the patients with non-Corona pulmonary diseases. Lastly, we elaborate on the existing challenges in the use of artificial intelligence for COVID-19 detection and the prospective trends of using this method in similar situations and conditions. Supplementary Information: The online version contains supplementary material available at 10.1007/s00521-023-08683-x.

Closed total talus dislocation without fracture: a case report.

Total dislocation of the talus from all of its joints is a rare injury specially when the talus and malleoli are not fractured and frequently it is as a result of a high-energy trauma. It usually leads to degenerative changes in neighboring joints and frequently avascular necrosis is a predictable outcome. We present a case of total talus dislocation because of a high-energy trauma in association with other major fractures resulting from a fall from height, but no fracture could be detected in the talus and any of malleols. Closed reduction was unsuccessful and we performed open reduction. At 6 month post operation follow-up, the talus didn't show subluxation and avascular necrosis could not be detected.

Pathologic dislocation of the shoulder secondary to septic arthritis: a case report.

Septic arthritis of the shoulder is uncommon in adults, and complete dislocation of the glenohumeral joint following septic arthritis is extremely rare. We report a case of pathologic shoulder dislocation secondary to septic arthritis in an intravenous drug abuser.

Germination polarity of Beauveria bassiana conidia and its possible correlation with virulence.

Three different germination types of conidia; unidirectional, bidirectional and multidirectional, were revealed through microscopic observations for eight Beauveria bassiana isolates germinated on Sabouraud dextrose agar. Canonical correlation analysis indicated that there is a positive correlation between unidirectional germination and virulence against diamondback moth, Plutella xylostella and the Colorado potato beetle, Leptinotarsa decemlineata. Scanning electron microscopy revealed different in vivo behaviors for unipolar- and bipolar-germinated conidia. Unipolar-germinated conidia produced a strong germ tube with mostly appressorium-like structures while bipolar-germinated conidia continued to invasive hyphal growth without any penetration, indicating that germination polarity in one way or another may be an indicator of pathogenic ability.