2023-09-23 更新
Enhancing Representation in Radiography-Reports Foundation Model: A Granular Alignment Algorithm Using Masked Contrastive Learning
Authors:Weijian Huang, Cheng Li, Hao Yang, Jiarun Liu, Shanshan Wang
Recently, multi-modal vision-language foundation models have gained significant attention in the medical field. While these models offer great opportunities, they still face a number of challenges, such as the requirement for fine-grained knowledge understanding in computer-aided diagnosis and capability of utilizing very limited or no task-specific labeled data in real-world clinical applications. In this study, we present MaCo, a novel multi-modal medical foundation model that explores masked contrastive learning to achieve granular alignment and zero-shot learning for a variety of medical imaging tasks. MaCo incorporates a correlation weighting mechanism to adjust the correlation between masked image patches and their corresponding reports, thereby enhancing the representation learning capabilities. We evaluate MaCo on six well-known open-source X-ray datasets, and the experimental results show it outperforms seven state-of-the-art approaches for classification, segmentation, and zero-shot phase grounding, demonstrating its great potential to promote a wide range of medical image analysis tasks.
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Learning from History: Task-agnostic Model Contrastive Learning for Image Restoration
Authors:Gang Wu, Junjun Jiang, Kui Jiang, Xianming Liu
Contrastive learning has emerged as a prevailing paradigm for high-level vision tasks, which, by introducing properly negative samples, has also been exploited for low-level vision tasks to achieve a compact optimization space to account for their ill-posed nature. However, existing methods rely on manually predefined, task-oriented negatives, which often exhibit pronounced task-specific biases. In this paper, we propose a innovative approach for the adaptive generation of negative samples directly from the target model itself, called learning from history. We introduce the Self-Prior guided Negative loss for image restoration (SPNIR) to enable this approach. Our approach is task-agnostic and generic, making it compatible with any existing image restoration method or task. We demonstrate the effectiveness of our approach by retraining existing models with SPNIR. The results show significant improvements in image restoration across various tasks and architectures. For example, models retrained with SPNIR outperform the original FFANet and DehazeFormer by 3.41 dB and 0.57 dB on the RESIDE indoor dataset for image dehazing. Similarly, they achieve notable improvements of 0.47 dB on SPA-Data over IDT for image deraining and 0.12 dB on Manga109 for a 4x scale super-resolution over lightweight SwinIR, respectively. Code and retrained models are available at https://github.com/Aitical/Task-agnostic_Model_Contrastive_Learning_Image_Restoration.
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Contrastive Deep Encoding Enables Uncertainty-aware Machine-learning-assisted Histopathology
Authors:Nirhoshan Sivaroopan, Chamuditha Jayanga, Chalani Ekanayake, Hasindri Watawana, Jathurshan Pradeepkumar, Mithunjha Anandakumar, Ranga Rodrigo, Chamira U. S. Edussooriya, Dushan N. Wadduwage
Deep neural network models can learn clinically relevant features from millions of histopathology images. However generating high-quality annotations to train such models for each hospital, each cancer type, and each diagnostic task is prohibitively laborious. On the other hand, terabytes of training data — while lacking reliable annotations — are readily available in the public domain in some cases. In this work, we explore how these large datasets can be consciously utilized to pre-train deep networks to encode informative representations. We then fine-tune our pre-trained models on a fraction of annotated training data to perform specific downstream tasks. We show that our approach can reach the state-of-the-art (SOTA) for patch-level classification with only 1-10% randomly selected annotations compared to other SOTA approaches. Moreover, we propose an uncertainty-aware loss function, to quantify the model confidence during inference. Quantified uncertainty helps experts select the best instances to label for further training. Our uncertainty-aware labeling reaches the SOTA with significantly fewer annotations compared to random labeling. Last, we demonstrate how our pre-trained encoders can surpass current SOTA for whole-slide image classification with weak supervision. Our work lays the foundation for data and task-agnostic pre-trained deep networks with quantified uncertainty.
PDF 18 pages, 8 figures
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Boosting Unsupervised Contrastive Learning Using Diffusion-Based Data Augmentation From Scratch
Authors:Zelin Zang, Hao Luo, Kai Wang, Panpan Zhang, Fan Wang, Stan. Z Li, Yang You
Unsupervised contrastive learning methods have recently seen significant improvements, particularly through data augmentation strategies that aim to produce robust and generalizable representations. However, prevailing data augmentation methods, whether hand designed or based on foundation models, tend to rely heavily on prior knowledge or external data. This dependence often compromises their effectiveness and efficiency. Furthermore, the applicability of most existing data augmentation strategies is limited when transitioning to other research domains, especially science-related data. This limitation stems from the paucity of prior knowledge and labeled data available in these domains. To address these challenges, we introduce DiffAug-a novel and efficient Diffusion-based data Augmentation technique. DiffAug aims to ensure that the augmented and original data share a smoothed latent space, which is achieved through diffusion steps. Uniquely, unlike traditional methods, DiffAug first mines sufficient prior semantic knowledge about the neighborhood. This provides a constraint to guide the diffusion steps, eliminating the need for labels, external data/models, or prior knowledge. Designed as an architecture-agnostic framework, DiffAug provides consistent improvements. Specifically, it improves image classification and clustering accuracy by 1.6%~4.5%. When applied to biological data, DiffAug improves performance by up to 10.1%, with an average improvement of 5.8%. DiffAug shows good performance in both vision and biological domains.
PDF arXiv admin note: text overlap with arXiv:2302.07944 by other authors
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3D Arterial Segmentation via Single 2D Projections and Depth Supervision in Contrast-Enhanced CT Images
Authors:Alina F. Dima, Veronika A. Zimmer, Martin J. Menten, Hongwei Bran Li, Markus Graf, Tristan Lemke, Philipp Raffler, Robert Graf, Jan S. Kirschke, Rickmer Braren, Daniel Rueckert
Automated segmentation of the blood vessels in 3D volumes is an essential step for the quantitative diagnosis and treatment of many vascular diseases. 3D vessel segmentation is being actively investigated in existing works, mostly in deep learning approaches. However, training 3D deep networks requires large amounts of manual 3D annotations from experts, which are laborious to obtain. This is especially the case for 3D vessel segmentation, as vessels are sparse yet spread out over many slices and disconnected when visualized in 2D slices. In this work, we propose a novel method to segment the 3D peripancreatic arteries solely from one annotated 2D projection per training image with depth supervision. We perform extensive experiments on the segmentation of peripancreatic arteries on 3D contrast-enhanced CT images and demonstrate how well we capture the rich depth information from 2D projections. We demonstrate that by annotating a single, randomly chosen projection for each training sample, we obtain comparable performance to annotating multiple 2D projections, thereby reducing the annotation effort. Furthermore, by mapping the 2D labels to the 3D space using depth information and incorporating this into training, we almost close the performance gap between 3D supervision and 2D supervision. Our code is available at: https://github.com/alinafdima/3Dseg-mip-depth.
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Multi-level Asymmetric Contrastive Learning for Medical Image Segmentation Pre-training
Authors:Shuang Zeng, Lei Zhu, Xinliang Zhang, Zifeng Tian, Qian Chen, Lujia Jin, Jiayi Wang, Yanye Lu
Contrastive learning, which is a powerful technique for learning image-level representations from unlabeled data, leads a promising direction to dealing with the dilemma between large-scale pre-training and limited labeled data. However, most existing contrastive learning strategies are designed mainly for downstream tasks of natural images, therefore they are sub-optimal and even worse than learning from scratch when directly applied to medical images whose downstream tasks are usually segmentation. In this work, we propose a novel asymmetric contrastive learning framework named JCL for medical image segmentation with self-supervised pre-training. Specifically, (1) A novel asymmetric contrastive learning strategy is proposed to pre-train both encoder and decoder simultaneously in one-stage to provide better initialization for segmentation models. (2) A multi-level contrastive loss is designed to take the correspondence among feature-level, image-level and pixel-level projections, respectively into account to make sure multi-level representations can be learned by the encoder and decoder during pre-training. (3) Experiments on multiple medical image datasets indicate our JCL framework outperforms existing SOTA contrastive learning strategies.
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