Multi-View Attention Learning for Residual Disease Prediction of Ovarian Cancer

• URL: http://arxiv.org/abs/2306.14646v1
• Date: Mon, 26 Jun 2023 12:31:08 GMT
• Title: Multi-View Attention Learning for Residual Disease Prediction of Ovarian Cancer
• Authors: Xiangneng Gao, Shulan Ruan, Jun Shi, Guoqing Hu, and Wei Wei
• Abstract summary: We propose a novel Multi-View Attention Learning (MuVAL) method for residual disease prediction. Our method focuses on the comprehensive learning of 3D Computed Tomography (CT) images in a multi-view manner. Experiments on a dataset of 111 patients show that our method outperforms existing deep-learning methods.
• Score: 8.697258566996126
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the treatment of ovarian cancer, precise residual disease prediction is significant for clinical and surgical decision-making. However, traditional methods are either invasive (e.g., laparoscopy) or time-consuming (e.g., manual analysis). Recently, deep learning methods make many efforts in automatic analysis of medical images. Despite the remarkable progress, most of them underestimated the importance of 3D image information of disease, which might brings a limited performance for residual disease prediction, especially in small-scale datasets. To this end, in this paper, we propose a novel Multi-View Attention Learning (MuVAL) method for residual disease prediction, which focuses on the comprehensive learning of 3D Computed Tomography (CT) images in a multi-view manner. Specifically, we first obtain multi-view of 3D CT images from transverse, coronal and sagittal views. To better represent the image features in a multi-view manner, we further leverage attention mechanism to help find the more relevant slices in each view. Extensive experiments on a dataset of 111 patients show that our method outperforms existing deep-learning methods.

Related papers

• Enhancing Multimodal Medical Image Classification using Cross-Graph Modal Contrastive Learning [5.660131312162423]
  This paper proposes a novel Cross-Graph Modal Contrastive Learning framework for multimodal medical image classification. The proposed approach is evaluated on two datasets: a Parkinson's disease (PD) dataset and a public melanoma dataset. Results demonstrate that CGMCL outperforms conventional unimodal methods in accuracy, interpretability, and early disease prediction.
  arXiv  Detail & Related papers  (2024-10-23T01:25:25Z)
• Autoregressive Sequence Modeling for 3D Medical Image Representation [48.706230961589924]
  We introduce a pioneering method for learning 3D medical image representations through an autoregressive sequence pre-training framework. Our approach various 3D medical images based on spatial, contrast, and semantic correlations, treating them as interconnected visual tokens within a token sequence.
  arXiv  Detail & Related papers  (2024-09-13T10:19:10Z)
• Multi-task Learning Approach for Intracranial Hemorrhage Prognosis [0.0]
  We propose a 3D multi-task image model to predict prognosis, Glasgow Coma Scale and age, improving accuracy and interpretability. Our method outperforms current state-of-the-art baseline image models, and demonstrates superior performance in ICH prognosis compared to four board-certified neuroradiologists using only CT scans as input.
  arXiv  Detail & Related papers  (2024-08-16T14:56:17Z)
• Enhancing Retinal Disease Classification from OCTA Images via Active Learning Techniques [0.8035416719640156]
  Eye diseases are common in older Americans and can lead to decreased vision and blindness. Recent advancements in imaging technologies allow clinicians to capture high-quality images of the retinal blood vessels via Optical Coherence Tomography Angiography ( OCTA) OCTA provides detailed vascular imaging as compared to the solely structural information obtained by common OCT imaging.
  arXiv  Detail & Related papers  (2024-07-21T23:24:49Z)
• Multi-View Vertebra Localization and Identification from CT Images [57.56509107412658]
  We propose a multi-view vertebra localization and identification from CT images. We convert the 3D problem into a 2D localization and identification task on different views. Our method can learn the multi-view global information naturally.
  arXiv  Detail & Related papers  (2023-07-24T14:43:07Z)
• LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching [59.01894976615714]
  We introduce LVM-Med, the first family of deep networks trained on large-scale medical datasets. We have collected approximately 1.3 million medical images from 55 publicly available datasets. LVM-Med empirically outperforms a number of state-of-the-art supervised, self-supervised, and foundation models.
  arXiv  Detail & Related papers  (2023-06-20T22:21:34Z)
• Self-supervised Learning from 100 Million Medical Images [13.958840691105992]
  We propose a method for self-supervised learning of rich image features based on contrastive learning and online feature clustering. We leverage large training datasets of over 100,000,000 medical images of various modalities, including radiography, computed tomography (CT), magnetic resonance (MR) imaging and ultrasonography. We highlight a number of advantages of this strategy on challenging image assessment problems in radiography, CT and MR.
  arXiv  Detail & Related papers  (2022-01-04T18:27:04Z)
• Incremental Cross-view Mutual Distillation for Self-supervised Medical CT Synthesis [88.39466012709205]
  This paper builds a novel medical slice to increase the between-slice resolution. Considering that the ground-truth intermediate medical slices are always absent in clinical practice, we introduce the incremental cross-view mutual distillation strategy. Our method outperforms state-of-the-art algorithms by clear margins.
  arXiv  Detail & Related papers  (2021-12-20T03:38:37Z)
• Deep Learning models for benign and malign Ocular Tumor Growth Estimation [3.1558405181807574]
  Clinicians often face issues in selecting suitable image processing algorithm for medical imaging data. A strategy for the selection of a proper model is presented here.
  arXiv  Detail & Related papers  (2021-07-09T05:40:25Z)
• A Multi-Stage Attentive Transfer Learning Framework for Improving COVID-19 Diagnosis [49.3704402041314]
  We propose a multi-stage attentive transfer learning framework for improving COVID-19 diagnosis. Our proposed framework consists of three stages to train accurate diagnosis models through learning knowledge from multiple source tasks and data of different domains. Importantly, we propose a novel self-supervised learning method to learn multi-scale representations for lung CT images.
  arXiv  Detail & Related papers  (2021-01-14T01:39:19Z)
• Spectral-Spatial Recurrent-Convolutional Networks for In-Vivo Hyperspectral Tumor Type Classification [49.32653090178743]
  We demonstrate the feasibility of in-vivo tumor type classification using hyperspectral imaging and deep learning. Our best model achieves an AUC of 76.3%, significantly outperforming previous conventional and deep learning methods.
  arXiv  Detail & Related papers  (2020-07-02T12:00:53Z)
• Review on Computer Vision in Gastric Cancer: Potential Efficient Tools for Diagnosis [0.0]
  This review focuses on advances in computer vision on gastric cancer. Different methods for data generation and augmentation are presented. Classification and segmentation techniques are discussed for assisting more precise diagnosis and timely treatment.
  arXiv  Detail & Related papers  (2020-05-17T16:14:15Z)

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.