Continually Evolved Multimodal Foundation Models for Cancer Prognosis
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866916593342087168 |
|---|---|
| author | Peng, Jie Zhou, Shuang Yang, Longwei Song, Yiran Zhang, Mohan Zhou, Kaixiong Xie, Feng Lin, Mingquan Zhang, Rui Chen, Tianlong |
| author_facet | Peng, Jie Zhou, Shuang Yang, Longwei Song, Yiran Zhang, Mohan Zhou, Kaixiong Xie, Feng Lin, Mingquan Zhang, Rui Chen, Tianlong |
| contents | Cancer prognosis is a critical task that involves predicting patient outcomes and survival rates. To enhance prediction accuracy, previous studies have integrated diverse data modalities, such as clinical notes, medical images, and genomic data, leveraging their complementary information. However, existing approaches face two major limitations. First, they struggle to incorporate newly arrived data with varying distributions into training, such as patient records from different hospitals, thus rendering sub-optimal generalizability and limited utility in real-world applications. Second, most multimodal integration methods rely on simplistic concatenation or task-specific pipelines, which fail to capture the complex interdependencies across modalities. To address these, we propose a continually evolving multi-modal foundation model. Extensive experiments on the TCGA dataset demonstrate the effectiveness of our approach, highlighting its potential to advance cancer prognosis by enabling robust and adaptive multimodal integration. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_18170 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Continually Evolved Multimodal Foundation Models for Cancer Prognosis Peng, Jie Zhou, Shuang Yang, Longwei Song, Yiran Zhang, Mohan Zhou, Kaixiong Xie, Feng Lin, Mingquan Zhang, Rui Chen, Tianlong Machine Learning I.2.7, J.3 Cancer prognosis is a critical task that involves predicting patient outcomes and survival rates. To enhance prediction accuracy, previous studies have integrated diverse data modalities, such as clinical notes, medical images, and genomic data, leveraging their complementary information. However, existing approaches face two major limitations. First, they struggle to incorporate newly arrived data with varying distributions into training, such as patient records from different hospitals, thus rendering sub-optimal generalizability and limited utility in real-world applications. Second, most multimodal integration methods rely on simplistic concatenation or task-specific pipelines, which fail to capture the complex interdependencies across modalities. To address these, we propose a continually evolving multi-modal foundation model. Extensive experiments on the TCGA dataset demonstrate the effectiveness of our approach, highlighting its potential to advance cancer prognosis by enabling robust and adaptive multimodal integration. |
| title | Continually Evolved Multimodal Foundation Models for Cancer Prognosis |
| topic | Machine Learning I.2.7, J.3 |
| url | https://arxiv.org/abs/2501.18170 |