Unified Medical Image-Text-Label Contrastive Learning With Continuous Prompt

• URL: http://arxiv.org/abs/2307.05920v1
• Date: Wed, 12 Jul 2023 05:19:10 GMT
• Title: Unified Medical Image-Text-Label Contrastive Learning With Continuous Prompt
• Authors: Yuhao Wang
• Abstract summary: We propose a unified Image-Text-Label contrastive learning framework based on continuous prompts. We demonstrate through sufficient experiments that the Unified Medical Contrastive Learning framework exhibits excellent performance on several downstream tasks.
• Score: 3.218449686637963
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Contrastive language-image Pre-training (CLIP) [13] can leverage large datasets of unlabeled Image-Text pairs, which have demonstrated impressive performance in various downstream tasks. Given that annotating medical data is time-consuming and laborious, Image-Text Pre-training has promising applications in exploiting large-scale medical image and radiology report datasets. However, medical Image-Text Pre-training faces several challenges, as follows: (1) Due to privacy concerns, the amount of available medical data is relatively small compared to natural data, leading to weaker generalization ability of the model. (2) Medical images are highly similar with only fine-grained differences in subtleties, resulting in a large number of false-negative sample pairs in comparison learning. (3) The hand-crafted Prompt usually differs from the natural medical image report, Subtle changes in wording can lead to significant differences in performance. In this paper, we propose a unified Image-Text-Label contrastive learning framework based on continuous prompts, with three main contributions. First, We unified the data of images, text, and labels, which greatly expanded the training data that the model could utilize. Second, we address the issue of data diversity and the impact of hand-crafted prompts on model performance by introducing continuous implicit prompts. Lastly, we propose a ImageText-Label contrastive Training to mitigate the problem of too many false-negative samples. We demonstrate through sufficient experiments that the Unified Medical Contrastive Learning (UMCL) framework exhibits excellent performance on several downstream tasks.

Related papers

• LoGra-Med: Long Context Multi-Graph Alignment for Medical Vision-Language Model [55.80651780294357]
  State-of-the-art medical multi-modal large language models (med-MLLM) leverage instruction-following data in pre-training. LoGra-Med is a new multi-graph alignment algorithm that enforces triplet correlations across image modalities, conversation-based descriptions, and extended captions. Our results show LoGra-Med matches LLAVA-Med performance on 600K image-text pairs for Medical VQA and significantly outperforms it when trained on 10% of the data.
  arXiv  Detail & Related papers  (2024-10-03T15:52:03Z)
• DOCCI: Descriptions of Connected and Contrasting Images [58.377060316967864]
  Descriptions of Connected and Contrasting Images (DOCCI) is a dataset with long, human-annotated English descriptions for 15k images. We instruct human annotators to create comprehensive descriptions for each image. We show that DOCCI is a useful testbed for text-to-image generation.
  arXiv  Detail & Related papers  (2024-04-30T17:56:24Z)
• MLIP: Medical Language-Image Pre-training with Masked Local Representation Learning [20.33625985769796]
  Existing contrastive language-image pre-training aims to learn a joint representation by matching abundant image-text pairs. We propose a Medical Language-Image Pre-training framework, which exploits the limited image-text medical data more efficiently. Our evaluation results show that MLIP outperforms previous work in zero/few-shot classification and few-shot segmentation tasks by a large margin.
  arXiv  Detail & Related papers  (2024-01-03T07:54:13Z)
• Multiscale Progressive Text Prompt Network for Medical Image Segmentation [10.121625177837931]
  We propose using progressive text prompts as prior knowledge to guide the segmentation process. Our model achieves high-quality results with low data annotation costs.
  arXiv  Detail & Related papers  (2023-06-30T23:37:16Z)
• Towards Unifying Medical Vision-and-Language Pre-training via Soft Prompts [63.84720380390935]
  There exist two typical types, textiti.e., the fusion-encoder type and the dual-encoder type, depending on whether a heavy fusion module is used. We propose an effective yet straightforward scheme named PTUnifier to unify the two types. We first unify the input format by introducing visual and textual prompts, which serve as a feature bank that stores the most representative images/texts.
  arXiv  Detail & Related papers  (2023-02-17T15:43:42Z)
• Learning to Exploit Temporal Structure for Biomedical Vision-Language Processing [53.89917396428747]
  Self-supervised learning in vision-language processing exploits semantic alignment between imaging and text modalities. We explicitly account for prior images and reports when available during both training and fine-tuning. Our approach, named BioViL-T, uses a CNN-Transformer hybrid multi-image encoder trained jointly with a text model.
  arXiv  Detail & Related papers  (2023-01-11T16:35:33Z)
• Texts as Images in Prompt Tuning for Multi-Label Image Recognition [70.9310322461598]
  We advocate that image-text contrastive learning makes it feasible to treat texts as images for prompt tuning and introduce TaI prompting. Particularly, we apply TaI prompting to multi-label image recognition, where sentences in the wild serve as alternatives to images for prompt tuning. Our proposed TaI-DPT outperforms zero-shot CLIP by a large margin on multiple benchmarks.
  arXiv  Detail & Related papers  (2022-11-23T07:00:11Z)
• Learning to Prompt for Vision-Language Models [82.25005817904027]
  Vision-language pre-training has emerged as a promising alternative for representation learning. It shifts from the tradition of using images and discrete labels for learning a fixed set of weights, seen as visual concepts, to aligning images and raw text for two separate encoders. Such a paradigm benefits from a broader source of supervision and allows zero-shot transfer to downstream tasks.
  arXiv  Detail & Related papers  (2021-09-02T17:57:31Z)
• Self-supervised Image-text Pre-training With Mixed Data In Chest X-rays [10.398175542736285]
  We introduce an image-text pre-training framework that can learn from mixed data inputs. We demonstrate the feasibility of pre-training across mixed data inputs. We also illustrate the benefits of adopting such pre-trained models in 3 chest X-ray applications.
  arXiv  Detail & Related papers  (2021-03-30T01:48:46Z)
• Contrastive Learning of Medical Visual Representations from Paired Images and Text [38.91117443316013]
  We propose ConVIRT, an unsupervised strategy to learn medical visual representations by exploiting naturally occurring descriptive paired text. Our new method of pretraining medical image encoders with the paired text data via a bidirectional contrastive objective between the two modalities is domain-agnostic, and requires no additional expert input.
  arXiv  Detail & Related papers  (2020-10-02T02:10:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.