Self-supervised learning (SSL) is a potential deep learning (DL) technique that uses
massive volumes of unlabeled data to train neural networks. SSL techniques have evolved …

Retinal vessel topology provides unique biological information for the diagnosis of fundus
diseases. However, most existing deep learning-based vessel segmentation methods …

Colorectal cancer is a leading cause of cancer-related deaths globally. In recent years, the
use of convolutional neural networks in computer-aided diagnosis (CAD) has facilitated …

Anomaly detection is an important task for medical image analysis, which can alleviate the
reliance of supervised methods on large labelled datasets. Most existing methods use a …

The fusion of optical coherence tomography (OCT) and fundus modality information can
provide a comprehensive diagnosis for retinal artery occlusion (RAO) disease, where OCT …

The conventional pretraining-and-finetuning paradigm, while effective for common diseases
with ample data, faces challenges in diagnosing data-scarce occupational diseases like …

Anomaly detection and segmentation in industrial images are critical tasks requiring robust
and precise methodologies. This paper presents the Multi-Scale Reverse Distillation …

Optical coherence tomography (OCT) has proven to be an effective and safe diagnostic tool
in clinical settings due to its unique advantages. Nonetheless, the OCT images are …

Monitoring the progression of glaucoma is crucial for preventing further vision loss.
However, deep learning-based models emphasize early glaucoma detection, resulting in a …

Anomaly detection (AD) is a fundamental task in computer vision. It aims to identify incorrect
image data patterns which deviate from the normal ones. Conventional methods generally …