D-PerceptCT: Deep Perceptual Enhancement for Low-Dose CT Images

Low Dose Computed Tomography (LDCT) is widely used as an imaging solution to aid diagnosis and other clinical tasks. However, this comes at the price of a deterioration in image quality due to the low dose of radiation used to reduce the risk of secondary cancer development. While some efficient methods have been proposed to enhance LDCT quality, many overestimate noise and perform excessive smoothing, leading to a loss of critical details. In this paper, we introduce D-PerceptCT, a novel architecture inspired by key principles of the Human Visual System (HVS) to enhance LDCT images. The objective is to guide the model to enhance or preserve perceptually relevant features, thereby providing radiologists with CT images where critical anatomical structures and fine pathological details are perceptu- ally visible. D-PerceptCT consists of two main blocks: 1) a Visual Dual-path Extractor (ViDex), which integrates semantic priors from a pretrained DINOv2 model with local spatial features, allowing the network to incorporate semantic-awareness during enhancement; (2) a Global-Local State-Space block that captures long-range information and multiscale features to preserve the important structures and fine details for diagnosis. In addition, we propose a novel deep perceptual loss, designated as the Deep Perceptual Relevancy Loss Function (DPRLF), which is inspired by human contrast sensitivity, to further emphasize perceptually important features. Extensive experiments on the Mayo2016 dataset demonstrate the effectiveness of D-PerceptCT method for LDCT enhancement, showing better preservation of structural and textural information within LDCT images compared to SOTA methods.

Self-supervised Learning for Gastrointestinal Pathologies Endoscopy Image Classification with Triplet Loss

Recently, the amount of GI tract datasets is introduced more and more by gathering from contests and challenges. The most common task needs to solve that is to classify images from the GI tract into various classes. However, the contributions of the existing approaches exhibit lots of limitations. In this paper, we aim to develop a computer-aided diagnosis system to classify the pathological findings in endoscopy images, the system can classify some common pathologies including polyps, esophagitis, and ulcerative -- colitis. To evaluate the proposed work, we use the public dataset which is Hyper--Kvasir instead of gathering the data. The key idea of our system is to develop self-supervised learning based on the Barlow Twins framework with a downstream task which is an endoscopy image classification integrated with triplet loss and focal loss functions. The self-supervision framework and focal loss function are used to overcome class-imbalanced data, while the triplet loss function is to tackle the domain-specific properties in endoscopy images which are inter/intra class problems. An extensive experimental study on the pathological finding images in the Hyper--Kvasir dataset has shown that our proposed system is in general better than the compared methods, whereas using a simple neural network model. This means the proposed system can be used efficiently and capable of accurately for the classification of pathology images in the GI tract.