相关论文: Multimodal Information Fusion for Glaucoma and DR …

Multimodal visual information fusion aims to integrate the multi-sensor data into a single image which contains more complementary information and less redundant features. However the complementary information is hard to extract, especially…

The analysis of fundus images is critical for the early detection and diagnosis of retinal diseases such as Diabetic Retinopathy (DR), Glaucoma, and Age-related Macular Degeneration (AMD). Traditional diagnostic workflows, however, often…

Machine learning methods in healthcare have traditionally focused on using data from a single modality, limiting their ability to effectively replicate the clinical practice of integrating multiple sources of information for improved…

Multimodal image fusion aims to combine relevant information from images acquired with different sensors. In medical imaging, fused images play an essential role in both standard and automated diagnosis. In this paper, we propose a novel…

Multi-modality is widely used in medical imaging, because it can provide multiinformation about a target (tumor, organ or tissue). Segmentation using multimodality consists of fusing multi-information to improve the segmentation. Recently,…

Multimodal medical image fusion plays a crucial role in medical diagnosis by integrating complementary information from different modalities to enhance image readability and clinical applicability. However, existing methods mainly follow…

Image analysis using more than one modality (i.e. multi-modal) has been increasingly applied in the field of biomedical imaging. One of the challenges in performing the multimodal analysis is that there exist multiple schemes for fusing the…

Color fundus photography and Optical Coherence Tomography (OCT) are the two most cost-effective tools for glaucoma screening. Both two modalities of images have prominent biomarkers to indicate glaucoma suspected. Clinically, it is often…

Glaucoma is one of the ophthalmic diseases that may cause blindness, for which early detection and treatment are very important. Fundus images and optical coherence tomography (OCT) images are both widely-used modalities in diagnosing…

Medical images play an important role in clinical applications. Multimodal medical images could provide rich information about patients for physicians to diagnose. The image fusion technique is able to synthesize complementary information…

This study aimed to enhance disease classification accuracy from retinal fundus images by integrating fine-grained image features and global textual context using a novel multimodal deep learning architecture. Existing multimodal large…

Medical image fusion combines the complementary information of multimodal medical images to assist medical professionals in the clinical diagnosis of patients' disorders and provide guidance during preoperative and intra-operative…

Multimodal fusion frameworks, which integrate diverse medical imaging modalities (e.g., MRI, CT), have shown great potential in applications such as skin cancer detection, dementia diagnosis, and brain tumor prediction. However, existing…

Medical patient data is always multimodal. Images, text, age, gender, histopathological data are only few examples for different modalities in this context. Processing and integrating this multimodal data with deep learning based methods is…

Glaucoma is a leading cause of irreversible blindness, but early detection can significantly improve treatment outcomes. Traditional diagnostic methods are often invasive and require specialized equipment. In this work, we present a deep…

This study introduces the Hybrid Multi-modal VGG (HM-VGG) model, a cutting-edge deep learning approach for the early diagnosis of glaucoma. The HM-VGG model utilizes an attention mechanism to process Visual Field (VF) data, enabling the…

Optical Coherence Tomography (OCT) is a novel and effective screening tool for ophthalmic examination. Since collecting OCT images is relatively more expensive than fundus photographs, existing methods use multi-modal learning to complement…

Multimodal pathological images are usually in clinical diagnosis, but computer vision-based multimodal image-assisted diagnosis faces challenges with modality fusion, especially in the absence of expert-annotated data. To achieve the…

Background/Objectives: Age-related macular degeneration, glaucoma, diabetic retinopathy (DR), diabetic macular edema, and pathological myopia affect hundreds of millions of people worldwide. Early screening for these diseases is essential,…

This paper proposes a novel multimodal fusion approach, aiming to produce best possible decisions by integrating information coming from multiple media. While most of the past multimodal approaches either work by projecting the features of…