Multi-Feature Vision Transformer via Self-Supervised Representation Learning for Improvement of COVID-19 Diagnosis

The role of chest X-ray (CXR) imaging, due to being more cost-effective, widely available, and having a faster acquisition time compared to CT, has evolved during the COVID-19 pandemic. To improve the diagnostic performance of CXR imaging a growing number of studies have investigated whether supervised deep learning methods can provide additional support. However, supervised methods rely on a large number of labeled radiology images, which is a time-consuming and complex procedure requiring expert clinician input. Due to the relative scarcity of COVID-19 patient data and the costly labeling process, self-supervised learning methods have gained momentum and has been proposed achieving comparable results to fully supervised learning approaches. In this work, we study the effectiveness of self-supervised learning in the context of diagnosing COVID-19 disease from CXR images. We propose a multi-feature Vision Transformer (ViT) guided architecture where we deploy a cross-attention mechanism to learn information from both original CXR images and corresponding enhanced local phase CXR images. We demonstrate the performance of the baseline self-supervised learning models can be further improved by leveraging the local phase-based enhanced CXR images. By using 10\% labeled CXR scans, the proposed model achieves 91.10\% and 96.21\% overall accuracy tested on total 35,483 CXR images of healthy (8,851), regular pneumonia (6,045), and COVID-19 (18,159) scans and shows significant improvement over state-of-the-art techniques. Code is available https://github.com/endiqq/Multi-Feature-ViTComment: Accepted to the 2022 MICCAI Workshop on Medical Image Learning with Limited and Noisy Dat

Multi-Scale Feature Fusion using Parallel-Attention Block for COVID-19 Chest X-ray Diagnosis

Under the global COVID-19 crisis, accurate diagnosis of COVID-19 from Chest X-ray (CXR) images is critical. To reduce intra- and inter-observer variability, during the radiological assessment, computer-aided diagnostic tools have been utilized to supplement medical decision-making and subsequent disease management. Computational methods with high accuracy and robustness are required for rapid triaging of patients and aiding radiologists in the interpretation of the collected data. In this study, we propose a novel multi-feature fusion network using parallel attention blocks to fuse the original CXR images and local-phase feature-enhanced CXR images at multi-scales. We examine our model on various COVID-19 datasets acquired from different organizations to assess the generalization ability. Our experiments demonstrate that our method achieves state-of-art performance and has improved generalization capability, which is crucial for widespread deployment.Comment: Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://melba-journal.org/2023:00

Antegrade pampiniform plexus venography in recurrent varicocele: Case report and anatomy review

Varicoceles are often treated with percutaneous embolization, using fibered coils and sclerosing agents, with the latter targeted at occlusion of pre-existing collateral veins. While various methods of surgical and embolization treatment are available, varicoceles may still recur from venous collateralization. We present a case, where following demonstration of complete occlusion of the right and left gonadal veins, direct puncture of the pampiniform venous plexus under ultrasound guidance revealed recurrent varicoceles supplied by anastomoses from the ipsilateral saphenous and femoral veins to the pampiniform plexus. In doing so, we describe a technique of percutaneous pampiniform venography in a case where the pertinent anatomy was not easily demonstrated by other methods

Balloon-assisted occlusion of the internal iliac arteries in patients with placenta accreta/percreta.

BACKGROUND: Placenta accreta/percreta is a leading cause of third trimester hemorrhage and postpartum maternal death. The current treatment for third trimester hemorrhage due to placenta accreta/percreta is cesarean hysterectomy, which may be complicated by large volume blood loss. PURPOSE: To determine what role, if any, prophylactic temporary balloon occlusion and transcatheter embolization of the anterior division of the internal iliac arteries plays in the management of patients with placenta accreta/percreta. METHODS: The records of 28 consecutive patients with a diagnosis of placenta accreta/percreta were retrospectively reviewed. Patients were divided into two groups. Six patients underwent prophylactic temporary balloon occlusion, followed by cesarean section, transcatheter embolization of the anterior division of the internal iliac arteries and cesarean hysterectomy (n = 5) or uterine curettage (n = 1). Twenty-two patients underwent cesarean hysterectomy without endovascular intervention. The following parameters were compared in the two groups: patient age, gravidity, parity, gestational age at delivery, days in the intensive care unit after delivery, total hospital days, volume of transfused blood products, volume of fluid replacement intraoperatively, operating room time, estimated blood loss, and postoperative morbidity and mortality. RESULTS: Patients in the embolization group had more frequent episodes of third trimester bleeding requiring admission and bedrest prior to delivery (16.7 days vs. 2.9 days), resulting in significantly more hospitalization time in the embolization group (23 days vs. 8.8 days) and delivery at an earlier gestational age than in those in the surgical group (32.5 weeks). There was no statistical difference in mean estimated blood loss, volume of replaced blood products, fluid replacement needs, operating room time or postoperative recovery time. CONCLUSION: Our findings do not support the contention that in patients with placenta accreta/percreta, prophylactic temporary balloon occlusion and embolization prior to hysterectomy diminishes intraoperative blood loss