A multimodal knowledge-enhanced whole-slide pathology foundation model
Remarkable strides in computational pathology have been made in the task-agnostic
foundation model that advances the performance of a wide array of downstream clinical …
foundation model that advances the performance of a wide array of downstream clinical …
Towards a generalizable pathology foundation model via unified knowledge distillation
Foundation models pretrained on large-scale datasets are revolutionizing the field of
computational pathology (CPath). The generalization ability of foundation models is crucial …
computational pathology (CPath). The generalization ability of foundation models is crucial …
Survmamba: State space model with multi-grained multi-modal interaction for survival prediction
Multi-modal learning that combines pathological images with genomic data has significantly
enhanced the accuracy of survival prediction. Nevertheless, existing methods have not fully …
enhanced the accuracy of survival prediction. Nevertheless, existing methods have not fully …
MEDFuse: Multimodal EHR data fusion with masked lab-test modeling and large language models
Electronic health records (EHRs) are multimodal by nature, consisting of structured tabular
features like lab tests and unstructured clinical notes. In real-life clinical practice, doctors use …
features like lab tests and unstructured clinical notes. In real-life clinical practice, doctors use …
[HTML][HTML] DF-DM: A foundational process model for multimodal data fusion in the artificial intelligence era
D Restrepo, C Wu, C Vásquez-Venegas… - Research …, 2024 - ncbi.nlm.nih.gov
In the big data era, integrating diverse data modalities poses significant challenges,
particularly in complex fields like healthcare. This paper introduces a new process model for …
particularly in complex fields like healthcare. This paper introduces a new process model for …
Multimodal Prototyping for cancer survival prediction
Multimodal survival methods combining gigapixel histology whole-slide images (WSIs) and
transcriptomic profiles are particularly promising for patient prognostication and stratification …
transcriptomic profiles are particularly promising for patient prognostication and stratification …
Mome: Mixture of multimodal experts for cancer survival prediction
Survival prediction requires integrating Whole Slide Images (WSIs) and genomics, a task
complicated by significant heterogeneity and complex inter-and intra-modal interactions …
complicated by significant heterogeneity and complex inter-and intra-modal interactions …
Agnostic-Specific Modality Learning for Cancer Survival Prediction from Multiple Data
Cancer is a pressing public health problem and one of the main causes of mortality
worldwide. The development of advanced computational methods for predicting cancer …
worldwide. The development of advanced computational methods for predicting cancer …
MM-Lego: Modular Biomedical Multimodal Models with Minimal Fine-Tuning
K Hemker, N Simidjievski, M Jamnik - arXiv preprint arXiv:2405.19950, 2024 - arxiv.org
Learning holistic computational representations in physical, chemical or biological systems
requires the ability to process information from different distributions and modalities within …
requires the ability to process information from different distributions and modalities within …
Cohort-Individual Cooperative Learning for Multimodal Cancer Survival Analysis
Recently, we have witnessed impressive achievements in cancer survival analysis by
integrating multimodal data, eg, pathology images and genomic profiles. However, the …
integrating multimodal data, eg, pathology images and genomic profiles. However, the …