LGIT: local-global interaction transformer for low-light image denoising.

Transformer-based methods effectively capture global dependencies in images, demonstrating outstanding performance in multiple visual tasks. However, existing Transformers cannot effectively denoise large noisy images captured under low-light conditions owing to (1) the global self-attention mechanism causing high computational complexity in the spatial dimension owing to a quadratic increase in computation with the number of tokens; (2) the channel-wise self-attention computation unable to optimise the spatial correlations in images. We propose a local-global interaction Transformer (LGIT) that employs an adaptive strategy to select relevant patches for global interaction, achieving low computational complexity in global self-attention computation.

MRI Generated From CT for Acute Ischemic Stroke Combining Radiomics and Generative Adversarial Networks.

Compared to computed tomography (CT), magnetic resonance imaging (MRI) is more sensitive to acute ischemic stroke lesion. However, MRI is time-consuming, expensive, and susceptible to interference from metal implants. Generating MRI images from CT images can address the limitations of MRI.

Radiomics model for preoperative prediction of 3-year survival-based CT image biomarkers in esophageal cancer.

Objective: This work aimed to develop a radiomics nomogram to predict 3-year overall survival of esophageal cancer patients after chemoradiotherapy.

Methods: A total of 109 esophageal cancer patients, diagnosed from November 2012 to February 2015, were enrolled in this retrospective study. They were randomly divided into training set (77 cases) and verification set (32 cases).

Differentiating Central Lung Tumors from Atelectasis with Contrast-Enhanced CT-Based Radiomics Features.

Objectives: To evaluate the utility of radiomics features in differentiating central lung cancers and atelectasis on contrast-enhanced computed tomography (CT) images. This study is retrospective.

Materials And Methods: In this study, 36 patients with central pulmonary cancer and atelectasis between July 2013 and June 2018 were identified.

Separability of Acute Cerebral Infarction Lesions in CT Based Radiomics: Toward Artificial Intelligence-Assisted Diagnosis.

This study aims at analyzing the separability of acute cerebral infarction lesions which were invisible in CT. 38 patients, who were diagnosed with acute cerebral infarction and performed both CT and MRI, and 18 patients, who had no positive finding in either CT or MRI, were enrolled. Comparative studies were performed on lesion and symmetrical regions, normal brain and symmetrical regions, lesion, and normal brain regions.

Diagnosis of Benign and Malignant Thyroid Nodules Using Combined Conventional Ultrasound and Ultrasound Elasticity Imaging.

Ultrasonography is one of the main imaging methods for diagnosing thyroid nodules. Automatic differentiation between benign and malignant nodules in ultrasound images can greatly assist inexperienced clinicians in their diagnosis. The key of problem is the effective utilization of the features of ultrasound images.

A Hadoop/MapReduce Based Platform for Supporting Health Big Data Analytics.

In this paper, we report our practical experience in designing and implementing a platform with Hadoop/MapReduce framework for supporting health Big Data Analytics. Three billion of emulated health raw data was constructed and cross-referenced with data profiles and metadata based on existing health data at the Island Health Authority, BC, Canada. The patient data was stored over a Hadoop Distributed File System to simulate a presentation of an entire health authority's information system.