Deep multimodal fusion of image and non-image data in disease diagnosis and prognosis: a review
The rapid development of diagnostic technologies in healthcare is leading to higher
requirements for physicians to handle and integrate the heterogeneous, yet complementary …
requirements for physicians to handle and integrate the heterogeneous, yet complementary …
A review of the application of multi-modal deep learning in medicine: bibliometrics and future directions
In recent years, deep learning has been applied in the field of clinical medicine to process
large-scale medical images, for large-scale data screening, and in the diagnosis and …
large-scale medical images, for large-scale data screening, and in the diagnosis and …
Multimodal dynamics: Dynamical fusion for trustworthy multimodal classification
Integration of heterogeneous and high-dimensional data (eg, multiomics) is becoming
increasingly important. Existing multimodal classification algorithms mainly focus on …
increasingly important. Existing multimodal classification algorithms mainly focus on …
Cross-modal translation and alignment for survival analysis
With the rapid advances in high-throughput sequencing technologies, the focus of survival
analysis has shifted from examining clinical indicators to incorporating genomic profiles with …
analysis has shifted from examining clinical indicators to incorporating genomic profiles with …
Shared-specific feature learning with bottleneck fusion transformer for multi-modal whole slide image analysis
Z Wang, L Yu, X Ding, X Liao… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
The fusion of multi-modal medical data is essential to assist medical experts to make
treatment decisions for precision medicine. For example, combining the whole slide …
treatment decisions for precision medicine. For example, combining the whole slide …
Integrative histology-genomic analysis predicts hepatocellular carcinoma prognosis using deep learning
Cancer prognosis analysis is of essential interest in clinical practice. In order to explore the
prognostic power of computational histopathology and genomics, this paper constructs a …
prognostic power of computational histopathology and genomics, this paper constructs a …
Smooth attention for deep multiple instance learning: Application to ct intracranial hemorrhage detection
Abstract Multiple Instance Learning (MIL) has been widely applied to medical imaging
diagnosis, where bag labels are known and instance labels inside bags are unknown …
diagnosis, where bag labels are known and instance labels inside bags are unknown …
Transformer-based personalized attention mechanism for medical images with clinical records
Y Takagi, N Hashimoto, H Masuda, H Miyoshi… - Journal of Pathology …, 2023 - Elsevier
In medical image diagnosis, identifying the attention region, ie, the region of interest for
which the diagnosis is made, is an important task. Various methods have been developed to …
which the diagnosis is made, is an important task. Various methods have been developed to …
[PDF][PDF] Dual Space Multiple Instance Representative Learning for Medical Image Classification.
Medical image classification plays a vital role in AI-aided medical diagnosis and is often
addressed as a Multiple Instance Learning (MIL) issue (ie, each sample is a bag of …
addressed as a Multiple Instance Learning (MIL) issue (ie, each sample is a bag of …
Multimodal Machine Learning in Image-Based and Clinical Biomedicine: Survey and Prospects
Abstract Machine learning (ML) applications in medical artificial intelligence (AI) systems
have shifted from traditional and statistical methods to increasing application of deep …
have shifted from traditional and statistical methods to increasing application of deep …