In response to the shortage of comprehensive data sets in the field of histopathology, a research team has introduced an innovative solution known as QUILT-1M. This new framework aims to take advantage of the wealth of information available on YouTube, particularly in the form of histopathology educational videos. By curating a massive dataset from these videos, QUILT-1M comprises an impressive 1 million paired image and text samples, making it the largest vision and language histopathology dataset to date. date.
The scarcity of such data sets has hampered progress in the field of histopathology, where dense, interconnected representations are essential for capturing the complexity of diverse disease subtypes. QUILT-1M offers several advantages. First, it does not overlap with existing data sources, ensuring a unique contribution to the knowledge of histopathology. Second, rich textual descriptions drawn from expert narratives within educational videos provide comprehensive information. Finally, multiple sentences per image offer diverse perspectives and in-depth understanding of each histopathological image.
The research team used a combination of models, algorithms, and human knowledge databases to curate this data set. They also expanded QUILT by adding data from other sources, including Twitter, research articles, and PubMed. The quality of the dataset is evaluated by several metrics, including ASR error rates, accuracy of language model corrections, and accuracy of subpathology classification.
In terms of results, QUILT-1M outperforms existing models, including BiomedCLIP, in zero-shot, linear probe, and cross-modal retrieval tasks across various types of subpathologies. QUILTNET performs better than state-of-the-art and reference out-of-domain CLIP histopathology models on 12 zero-shot tasks, covering 8 different subpathologies. The research team emphasizes the potential of QUILT-1M to benefit both computer scientists and histopathologists.
In conclusion, QUILT-1M represents a significant advance in the field of histopathology by providing a large, diverse, and high-quality vision and language data set. It opens new possibilities for research and development of more effective histopathological models.
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Pragati Jhunjhunwala is a Consulting Intern at MarktechPost. She is currently pursuing B.tech from the Indian Institute of technology (IIT), Kharagpur. She is a technology enthusiast and has a keen interest in the scope of data science software and applications. She is always reading about the advancements in different fields of ai and ML.
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