A New Era in Computational Pathology: A Survey on Foundation and Vision-Language Models
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910609135632384 |
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| author | Chanda, Dibaloke Aryal, Milan Soltani, Nasim Yahya Ganji, Masoud |
| author_facet | Chanda, Dibaloke Aryal, Milan Soltani, Nasim Yahya Ganji, Masoud |
| contents | Recent advances in deep learning have completely transformed the domain of computational pathology (CPath). More specifically, it has altered the diagnostic workflow of pathologists by integrating foundation models (FMs) and vision-language models (VLMs) in their assessment and decision-making process. The limitations of existing deep learning approaches in CPath can be overcome by FMs through learning a representation space that can be adapted to a wide variety of downstream tasks without explicit supervision. Deploying VLMs allow pathology reports written in natural language be used as rich semantic information sources to improve existing models as well as generate predictions in natural language form. In this survey, a holistic and systematic overview of recent innovations in FMs and VLMs in CPath is presented. Furthermore, the tools, datasets and training schemes for these models are summarized in addition to categorizing them into distinct groups. This extensive survey highlights the current trends in CPath and its possible revolution through the use of FMs and VLMs in the future. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_14496 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A New Era in Computational Pathology: A Survey on Foundation and Vision-Language Models Chanda, Dibaloke Aryal, Milan Soltani, Nasim Yahya Ganji, Masoud Machine Learning Artificial Intelligence Computation and Language Image and Video Processing Recent advances in deep learning have completely transformed the domain of computational pathology (CPath). More specifically, it has altered the diagnostic workflow of pathologists by integrating foundation models (FMs) and vision-language models (VLMs) in their assessment and decision-making process. The limitations of existing deep learning approaches in CPath can be overcome by FMs through learning a representation space that can be adapted to a wide variety of downstream tasks without explicit supervision. Deploying VLMs allow pathology reports written in natural language be used as rich semantic information sources to improve existing models as well as generate predictions in natural language form. In this survey, a holistic and systematic overview of recent innovations in FMs and VLMs in CPath is presented. Furthermore, the tools, datasets and training schemes for these models are summarized in addition to categorizing them into distinct groups. This extensive survey highlights the current trends in CPath and its possible revolution through the use of FMs and VLMs in the future. |
| title | A New Era in Computational Pathology: A Survey on Foundation and Vision-Language Models |
| topic | Machine Learning Artificial Intelligence Computation and Language Image and Video Processing |
| url | https://arxiv.org/abs/2408.14496 |