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Cyclic Generative Adversarial Networks With Congruent Image-Report Generation For Explainable Medical Image Analysis

arXiv 2211.08424 ↗First posted 16 Nov 2022eess.IV cs.CVcited by 0

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Abstract (arXiv)

We present a novel framework for explainable labeling and interpretation of medical images. Medical images require specialized professionals for interpretation, and are explained (typically) via elaborate textual reports. Different from prior methods that focus on medical report generation from images or vice-versa, we novelly generate congruent image--report pairs employing a cyclic-Generative Adversarial Network (cycleGAN); thereby, the generated report will adequately explain a medical image, while a report-generated image that effectively characterizes the text visually should (sufficiently) resemble the original. The aim of the work is to generate trustworthy and faithful explanations for the outputs of a model diagnosing chest x-ray images by pointing a human user to similar cases in support of a diagnostic decision. Apart from enabling transparent medical image labeling and interpretation, we achieve report and image-based labeling comparable to prior methods, including state-of-the-art performance in some cases as evidenced by experiments on the Indiana Chest X-ray dataset

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References · 34

  1. 2017CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learningcited by 459
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22 more
  1. 2019Adversarial Pulmonary Pathology Translation for Pairwise Chest X-ray Data Augmentationcited by 11
  2. 2020Registration of Histopathogy Images Using Structural Information From Fine Grained Feature Mapscited by 11
  3. 2019Informative sample generation using class aware generative adversarial networks for classification of chest Xrayscited by 10
  4. 2022Probabilistic Integration of Object Level Annotations in Chest X-ray Classificationcited by 10
  5. 2017Retinal Vasculature Segmentation Using Local Saliency Maps and Generative Adversarial Networks For Image Super Resolutioncited by 9
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  12. 2021CT Image Synthesis Using Weakly Supervised Segmentation and Geometric Inter-Label Relations For COVID Image Analysiscited by 8
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  14. 2021Multi-scale Deep Learning Architecture for Nucleus Detection in Renal Cell Carcinoma Microscopy Imagecited by 7
  15. 2021Gated Fusion Network for SAO Filter and Inter Frame Prediction in Versatile Video Codingcited by 6
  16. 2022Anomaly Detection in Retinal Images using Multi-Scale Deep Feature Sparse Codingcited by 6
  17. 2021Learning of Inter-Label Geometric Relationships Using Self-Supervised Learning: Application To Gleason Grade Segmentationcited by 5
  18. 2022Outlier-based Autism Detection using Longitudinal Structural MRIcited by 5
  19. 2022Unsupervised Domain Adaptation Using Feature Disentanglement And GCNs For Medical Image Classificationcited by 5
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  22. 2022Improved Super Resolution of MR Images Using CNNs and Vision Transformerscited by 1

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