2022-07-13 更新
Unsupervised Joint Image Transfer and Uncertainty Quantification using Patch Invariant Networks
Authors:Christoph Angermann, Markus Haltmeier, Ahsan Raza Siyal
Unsupervised image transfer enables intra- and inter-modality transfer for medical applications where a large amount of paired training data is not abundant. To ensure a structure-preserving mapping from the input to the target domain, existing methods for unpaired medical image transfer are commonly based on cycle-consistency, causing additional computation resources and instability due to the learning of an inverse mapping. This paper presents a novel method for uni-directional domain mapping where no paired data is needed throughout the entire training process. A reasonable transfer is ensured by employing the GAN architecture and a novel generator loss based on patch invariance. To be more precise, generator outputs are evaluated and compared on different scales, which brings increased attention to high-frequency details as well as implicit data augmentation. This novel term also gives the opportunity to predict aleatoric uncertainty by modeling an input-dependent scale map for the patch residuals. The proposed method is comprehensively evaluated on three renowned medical databases. Superior accuracy on these datasets compared to four different state-of-the-art methods for unpaired image transfer suggests the great potential of this approach for uncertainty-aware medical image translation. Implementation of the proposed framework is released here: https://github.com/anger-man/unsupervised-image-transfer-and-uq.
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PI-Trans: Parallel-ConvMLP and Implicit-Transformation Based GAN for Cross-View Image Translation
Authors:Bin Ren, Hao Tang, Yiming Wang, Xia Li, Wei Wang, Nicu Sebe
For semantic-guided cross-view image translation, it is crucial to learn where to sample pixels from the source view image and where to reallocate them guided by the target view semantic map, especially when there is little overlap or drastic view difference between the source and target images. Hence, one not only needs to encode the long-range dependencies among pixels in both the source view image and target view the semantic map but also needs to translate these learned dependencies. To this end, we propose a novel generative adversarial network, PI-Trans, which mainly consists of a novel Parallel-ConvMLP module and an Implicit Transformation module at multiple semantic levels. Extensive experimental results show that the proposed PI-Trans achieves the best qualitative and quantitative performance by a large margin compared to the state-of-the-art methods on two challenging datasets. The code will be made available at https://github.com/Amazingren/PI-Trans.
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GAN-based Virtual Re-Staining: A Promising Solution for Whole Slide Image Analysis
Authors:Zhaoyang Xu, Xingru Huang, Carlos Fernández Moro, Béla Bozóky, Qianni Zhang
Histopathological cancer diagnosis is based on visual examination of stained tissue slides. Hematoxylin and eosin (H\&E) is a standard stain routinely employed worldwide. It is easy to acquire and cost effective, but cells and tissue components show low-contrast with varying tones of dark blue and pink, which makes difficult visual assessments, digital image analysis, and quantifications. These limitations can be overcome by IHC staining of target proteins of the tissue slide. IHC provides a selective, high-contrast imaging of cells and tissue components, but their use is largely limited by a significantly more complex laboratory processing and high cost. We proposed a conditional CycleGAN (cCGAN) network to transform the H\&E stained images into IHC stained images, facilitating virtual IHC staining on the same slide. This data-driven method requires only a limited amount of labelled data but will generate pixel level segmentation results. The proposed cCGAN model improves the original network \cite{zhu_unpaired_2017} by adding category conditions and introducing two structural loss functions, which realize a multi-subdomain translation and improve the translation accuracy as well. % need to give reasons here. Experiments demonstrate that the proposed model outperforms the original method in unpaired image translation with multi-subdomains. We also explore the potential of unpaired images to image translation method applied on other histology images related tasks with different staining techniques.
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