A Strictly Bounded Deep Network for Unpaired Cyclic Translation of Medical Images

• URL: http://arxiv.org/abs/2311.02480v1
• Date: Sat, 4 Nov 2023 18:43:31 GMT
• Title: A Strictly Bounded Deep Network for Unpaired Cyclic Translation of Medical Images
• Authors: Swati Rai, Jignesh S. Bhatt, and Sarat Kumar Patra
• Abstract summary: We consider unpaired medical images and provide a strictly bounded network that yields a stable bidirectional translation. We propose a patch-level exploitd cyclic conditional adversarial network (pCCGAN) embedded with adaptive dictionary learning.
• Score: 0.5120567378386615
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Medical image translation is an ill-posed problem. Unlike existing paired unbounded unidirectional translation networks, in this paper, we consider unpaired medical images and provide a strictly bounded network that yields a stable bidirectional translation. We propose a patch-level concatenated cyclic conditional generative adversarial network (pCCGAN) embedded with adaptive dictionary learning. It consists of two cyclically connected CGANs of 47 layers each; where both generators (each of 32 layers) are conditioned with concatenation of alternate unpaired patches from input and target modality images (not ground truth) of the same organ. The key idea is to exploit cross-neighborhood contextual feature information that bounds the translation space and boosts generalization. The generators are further equipped with adaptive dictionaries learned from the contextual patches to reduce possible degradation. Discriminators are 15-layer deep networks that employ minimax function to validate the translated imagery. A combined loss function is formulated with adversarial, non-adversarial, forward-backward cyclic, and identity losses that further minimize the variance of the proposed learning machine. Qualitative, quantitative, and ablation analysis show superior results on real CT and MRI.

Related papers

• Spatial Semantic Recurrent Mining for Referring Image Segmentation [63.34997546393106]
  We propose Stextsuperscript2RM to achieve high-quality cross-modality fusion. It follows a working strategy of trilogy: distributing language feature, spatial semantic recurrent coparsing, and parsed-semantic balancing. Our proposed method performs favorably against other state-of-the-art algorithms.
  arXiv  Detail & Related papers  (2024-05-15T00:17:48Z)
• SyntStereo2Real: Edge-Aware GAN for Remote Sensing Image-to-Image Translation while Maintaining Stereo Constraint [1.8749305679160366]
  Current methods involve combining two networks, an unpaired image-to-image translation network and a stereo-matching network. We propose an edge-aware GAN-based network that effectively tackles both tasks simultaneously. We demonstrate that our model produces qualitatively and quantitatively superior results than existing models, and its applicability extends to diverse domains.
  arXiv  Detail & Related papers  (2024-04-14T14:58:52Z)
• Anatomical Conditioning for Contrastive Unpaired Image-to-Image Translation of Optical Coherence Tomography Images [0.0]
  We study the problem employing an optical coherence tomography ( OCT) data set of Spectralis- OCT and Home- OCT images. I2I translation is challenging because the images are unpaired. Our approach increases the similarity between the style-translated images and the target distribution.
  arXiv  Detail & Related papers  (2024-04-08T11:20:28Z)
• Cascaded Multi-path Shortcut Diffusion Model for Medical Image Translation [26.67518950976257]
  We propose a Cascade Multi-path Shortcut Diffusion Model (CMDM) for high-quality medical image translation and uncertainty estimation. Our experimental results found that CMDM can produce high-quality translations comparable to state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-04-06T03:02:47Z)
• So Different Yet So Alike! Constrained Unsupervised Text Style Transfer [54.4773992696361]
  We introduce a method for constrained unsupervised text style transfer by introducing two complementary losses to the generative adversarial network (GAN) family of models. Unlike the competing losses used in GANs, we introduce cooperative losses where the discriminator and the generator cooperate and reduce the same loss. We show that the complementary cooperative losses improve text quality, according to both automated and human evaluation measures.
  arXiv  Detail & Related papers  (2022-05-09T07:46:40Z)
• The Spatially-Correlative Loss for Various Image Translation Tasks [69.62228639870114]
  We propose a novel spatially-correlative loss that is simple, efficient and yet effective for preserving scene structure consistency. Previous methods attempt this by using pixel-level cycle-consistency or feature-level matching losses. We show distinct improvement over baseline models in all three modes of unpaired I2I translation: single-modal, multi-modal, and even single-image translation.
  arXiv  Detail & Related papers  (2021-04-02T02:13:30Z)
• Segmentation-Renormalized Deep Feature Modulation for Unpaired Image Harmonization [0.43012765978447565]
  Cycle-consistent Generative Adversarial Networks have been used to harmonize image sets between a source and target domain. These methods are prone to instability, contrast inversion, intractable manipulation of pathology, and steganographic mappings which limit their reliable adoption in real-world medical imaging. We propose a segmentation-renormalized image translation framework to reduce inter-scanner harmonization while preserving anatomical layout.
  arXiv  Detail & Related papers  (2021-02-11T23:53:51Z)
• Flow-based Deformation Guidance for Unpaired Multi-Contrast MRI Image-to-Image Translation [7.8333615755210175]
  In this paper, we introduce a novel approach to unpaired image-to-image translation based on the invertible architecture. We utilize the temporal information between consecutive slices to provide more constraints to the optimization for transforming one domain to another in unpaired medical images.
  arXiv  Detail & Related papers  (2020-12-03T09:10:22Z)
• Collaborative Boundary-aware Context Encoding Networks for Error Map Prediction [65.44752447868626]
  We propose collaborative boundaryaware context encoding networks called AEP-Net for error prediction task. Specifically, we propose a collaborative feature transformation branch for better feature fusion between images and masks, and precise localization of error regions. The AEP-Net achieves an average DSC of 0.8358, 0.8164 for error prediction task, and shows a high Pearson correlation coefficient of 0.9873.
  arXiv  Detail & Related papers  (2020-06-25T12:42:01Z)
• StereoGAN: Bridging Synthetic-to-Real Domain Gap by Joint Optimization of Domain Translation and Stereo Matching [56.95846963856928]
  Large-scale synthetic datasets are beneficial to stereo matching but usually introduce known domain bias. We propose an end-to-end training framework with domain translation and stereo matching networks to tackle this challenge.
  arXiv  Detail & Related papers  (2020-05-05T03:11:38Z)
• ElixirNet: Relation-aware Network Architecture Adaptation for Medical Lesion Detection [90.13718478362337]
  We introduce a novel ElixirNet that includes three components: 1) TruncatedRPN balances positive and negative data for false positive reduction; 2) Auto-lesion Block is automatically customized for medical images to incorporate relation-aware operations among region proposals; and 3) Relation transfer module incorporates the semantic relationship. Experiments on DeepLesion and Kits19 prove the effectiveness of ElixirNet, achieving improvement of both sensitivity and precision over FPN with fewer parameters.
  arXiv  Detail & Related papers  (2020-03-03T05:29:49Z)

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.