Image Quality-aware Diagnosis via Meta-knowledge Co-embedding

• URL: http://arxiv.org/abs/2303.15038v2
• Date: Fri, 14 Apr 2023 07:38:18 GMT
• Title: Image Quality-aware Diagnosis via Meta-knowledge Co-embedding
• Authors: Haoxuan Che, Siyu Chen, Hao Chen
• Abstract summary: We propose a novel meta-knowledge co-embedding network, consisting of twos: Task Net and Meta Learner. Task Net constructs an explicit quality information utilization mechanism to enhance diagnosis via knowledge co-embedding features. Meta Learner ensures the effectiveness and constrains the semantics of these features via meta-learning and joint-encoding masking.
• Score: 11.14366093273983
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Medical images usually suffer from image degradation in clinical practice, leading to decreased performance of deep learning-based models. To resolve this problem, most previous works have focused on filtering out degradation-causing low-quality images while ignoring their potential value for models. Through effectively learning and leveraging the knowledge of degradations, models can better resist their adverse effects and avoid misdiagnosis. In this paper, we raise the problem of image quality-aware diagnosis, which aims to take advantage of low-quality images and image quality labels to achieve a more accurate and robust diagnosis. However, the diversity of degradations and superficially unrelated targets between image quality assessment and disease diagnosis makes it still quite challenging to effectively leverage quality labels to assist diagnosis. Thus, to tackle these issues, we propose a novel meta-knowledge co-embedding network, consisting of two subnets: Task Net and Meta Learner. Task Net constructs an explicit quality information utilization mechanism to enhance diagnosis via knowledge co-embedding features, while Meta Learner ensures the effectiveness and constrains the semantics of these features via meta-learning and joint-encoding masking. Superior performance on five datasets with four widely-used medical imaging modalities demonstrates the effectiveness and generalizability of our method.

Related papers

• A Clinical-oriented Multi-level Contrastive Learning Method for Disease Diagnosis in Low-quality Medical Images [4.576524795036682]
  Disease diagnosis methods guided by contrastive learning (CL) have shown significant advantages in lesion feature representation. We propose a clinical-oriented multi-level CL framework that aims to enhance the model's capacity to extract lesion features. The proposed CL framework is validated on two public medical image datasets, EyeQ and Chest X-ray.
  arXiv  Detail & Related papers  (2024-04-07T09:08:14Z)
• Adversarial-Robust Transfer Learning for Medical Imaging via Domain Assimilation [17.46080957271494]
  The scarcity of publicly available medical images has led contemporary algorithms to depend on pretrained models grounded on a large set of natural images. A significant em domain discrepancy exists between natural and medical images, which causes AI models to exhibit heightened em vulnerability to adversarial attacks. This paper proposes a em domain assimilation approach that introduces texture and color adaptation into transfer learning, followed by a texture preservation component to suppress undesired distortion.
  arXiv  Detail & Related papers  (2024-02-25T06:39:15Z)
• Multi-modal Medical Neurological Image Fusion using Wavelet Pooled Edge Preserving Autoencoder [3.3828292731430545]
  This paper presents an end-to-end unsupervised fusion model for multimodal medical images based on an edge-preserving dense autoencoder network. In the proposed model, feature extraction is improved by using wavelet decomposition-based attention pooling of feature maps. The proposed model is trained on a variety of medical image pairs which helps in capturing the intensity distributions of the source images.
  arXiv  Detail & Related papers  (2023-10-18T11:59:35Z)
• Causality-Driven One-Shot Learning for Prostate Cancer Grading from MRI [1.049712834719005]
  We present a novel method to automatically classify medical images that learns and leverages weak causal signals in the image. Our framework consists of a convolutional neural network backbone and a causality-extractor module. Our findings show that causal relationships among features play a crucial role in enhancing the model's ability to discern relevant information.
  arXiv  Detail & Related papers  (2023-09-19T16:08:33Z)
• Helping Visually Impaired People Take Better Quality Pictures [52.03016269364854]
  We develop tools to help visually impaired users minimize occurrences of common technical distortions. We also create a prototype feedback system that helps to guide users to mitigate quality issues.
  arXiv  Detail & Related papers  (2023-05-14T04:37:53Z)
• Self-supervised Domain Adaptation for Breaking the Limits of Low-quality Fundus Image Quality Enhancement [14.677912534121273]
  Low-quality fundus images and style inconsistency potentially increase uncertainty in the diagnosis of fundus disease. We formulate two self-supervised domain adaptation tasks to disentangle the features of image content, low-quality factor and style information. Our DASQE method achieves new state-of-the-art performance when only low-quality images are available.
  arXiv  Detail & Related papers  (2023-01-17T15:07:20Z)
• Harmonizing Pathological and Normal Pixels for Pseudo-healthy Synthesis [68.5287824124996]
  We present a new type of discriminator, the segmentor, to accurately locate the lesions and improve the visual quality of pseudo-healthy images. We apply the generated images into medical image enhancement and utilize the enhanced results to cope with the low contrast problem. Comprehensive experiments on the T2 modality of BraTS demonstrate that the proposed method substantially outperforms the state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-03-29T08:41:17Z)
• Explaining Clinical Decision Support Systems in Medical Imaging using Cycle-Consistent Activation Maximization [112.2628296775395]
  Clinical decision support using deep neural networks has become a topic of steadily growing interest. clinicians are often hesitant to adopt the technology because its underlying decision-making process is considered to be intransparent and difficult to comprehend. We propose a novel decision explanation scheme based on CycleGAN activation which generates high-quality visualizations of classifier decisions even in smaller data sets.
  arXiv  Detail & Related papers  (2020-10-09T14:39:27Z)
• Multi-label Thoracic Disease Image Classification with Cross-Attention Networks [65.37531731899837]
  We propose a novel scheme of Cross-Attention Networks (CAN) for automated thoracic disease classification from chest x-ray images. We also design a new loss function that beyond cross-entropy loss to help cross-attention process and is able to overcome the imbalance between classes and easy-dominated samples within each class.
  arXiv  Detail & Related papers  (2020-07-21T14:37:00Z)
• Collaborative Unsupervised Domain Adaptation for Medical Image Diagnosis [102.40869566439514]
  We seek to exploit rich labeled data from relevant domains to help the learning in the target task via Unsupervised Domain Adaptation (UDA) Unlike most UDA methods that rely on clean labeled data or assume samples are equally transferable, we innovatively propose a Collaborative Unsupervised Domain Adaptation algorithm. We theoretically analyze the generalization performance of the proposed method, and also empirically evaluate it on both medical and general images.
  arXiv  Detail & Related papers  (2020-07-05T11:49:17Z)
• Semi-supervised Medical Image Classification with Relation-driven Self-ensembling Model [71.80319052891817]
  We present a relation-driven semi-supervised framework for medical image classification. It exploits the unlabeled data by encouraging the prediction consistency of given input under perturbations. Our method outperforms many state-of-the-art semi-supervised learning methods on both single-label and multi-label image classification scenarios.
  arXiv  Detail & Related papers  (2020-05-15T06:57:54Z)

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.