Summary of Cplip: Zero-shot Learning For Histopathology with Comprehensive Vision-language Alignment, by Sajid Javed et al.
CPLIP: Zero-Shot Learning for Histopathology with Comprehensive Vision-Language Alignment
by Sajid Javed, Arif Mahmood, Iyyakutti Iyappan Ganapathi, Fayaz Ali Dharejo, Naoufel Werghi, Mohammed Bennamoun
First submitted to arxiv on: 7 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG); Multimedia (cs.MM); Image and Video Processing (eess.IV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Comprehensive Pathology Language Image Pre-training (CPLIP) is a novel unsupervised technique designed to enhance the alignment of images and text in histopathology for tasks such as classification and segmentation. The methodology enriches vision-language models by leveraging extensive data without needing ground truth annotations. CPLIP involves constructing a pathology-specific dictionary, generating textual descriptions for images using language models, and retrieving relevant images for each text snippet via a pre-trained model. The model is then fine-tuned using a many-to-many contrastive learning method to align complex interrelated concepts across both modalities. Evaluated across multiple histopathology tasks, CPLIP shows notable improvements in zero-shot learning scenarios, outperforming existing methods in both interpretability and robustness and setting a higher benchmark for the application of vision-language models in the field. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Comprehensive Pathology Language Image Pre-training (CPLIP) is a new way to connect pictures and text from medical tests. It helps computers understand what’s going on in pictures by using lots of data without needing special labels. This makes it better at doing tasks like classifying or segmenting images, especially when there’s no training data. The idea involves making a special dictionary for medical terms, generating text to describe the pictures, and finding matching pictures for each text snippet. It’s then fine-tuned to make sure the concepts match up. CPLIP did really well in tests on different medical tasks and is now a new standard for using computers to understand medical images. |
Keywords
» Artificial intelligence » Alignment » Classification » Unsupervised » Zero shot
