Chest X-ray Inpainting with Deep Generative Models

Sogancioglu, Ecem; Shi, Hang; Belli, Davide; Ginneken, Bram van

doi:10.48550/arxiv.1809.01471

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…However, this is already feasible for many types of distributions. For example, image inpainting is a subfield of computer vision that has a long history [13] and much recent work (e.g., [32,31,30,28,23,33]), with plausible in-fill models available for many domains. As these generative models improve, so will the framework we proposed.…”

Section: Discussionmentioning

confidence: 99%

Interpreting Black Box Models via Hypothesis Testing

Burns,

Thomason,

Tansey

2019

Preprint

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While many methods for interpreting machine learning models have been proposed, they are often ad hoc, difficult to interpret, and come with limited guarantees. This is especially problematic in science and medicine, where model interpretations may be reported as discoveries or guide patient treatments. As a step toward more principled and reliable interpretations, in this paper we reframe black box model interpretability as a multiple hypothesis testing problem. The task is to discover "important" features by testing whether the model prediction is significantly different from what would be expected if the features were replaced with uninformative counterfactuals. We propose two testing methods: one that provably controls the false discovery rate but which is not yet feasible for large-scale applications, and an approximate testing method which can be applied to real-world data sets. In simulation, both tests have high power relative to existing interpretability methods. When applied to state-of-the-art vision and language models, the framework selects features that intuitively explain model predictions. The resulting explanations have the additional advantage that they are themselves easy to interpret.Preprint. Under review.

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Section: Discussionmentioning

confidence: 99%

Interpreting Black Box Models via Hypothesis Testing

Burns,

Thomason,

Tansey

2019

Preprint

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“…Image inpainting can edit a local region only given its surroundings in a patch and keep the original spatial resolution unchanged, which fits highresolution CXR images perfectly. Sogancioglu et al [36] firstly investigated the performance of several image inpainting models [37]- [39] applied to CXR images, and demonstrated the feasibility of generating realistic and seamless patches based on image inpainting. Gundel et al [16] then proposed a local feature augmentation method.…”

Section: B Lung Nodule Synthesismentioning

confidence: 99%

Image Synthesis with Disentangled Attributes for Chest X-Ray Nodule Augmentation and Detection

Shen¹,

Ouyang²,

Xiao³

et al. 2022

Preprint

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Lung nodule detection in chest X-ray (CXR) images is common to early screening of lung cancers. Deep-learningbased Computer-Assisted Diagnosis (CAD) systems can support radiologists for nodule screening in CXR. However, it requires large-scale and diverse medical data with high-quality annotations to train such robust and accurate CADs. To alleviate the limited availability of such datasets, lung nodule synthesis methods are proposed for the sake of data augmentation. Nevertheless, previous methods lack the ability to generate nodules that are realistic with the shape/size attributes desired by the detector. To address this issue, we introduce a novel lung nodule synthesis framework in this paper, which decomposes nodule attributes into three main aspects including shape, size, and texture, respectively. A GAN-based Shape Generator firstly models nodule shapes by generating diverse shape masks. The following Size Modulation then enables quantitative control on the diameters of the generated nodule shapes in pixel-level granularity. A coarseto-fine gated convolutional Texture Generator finally synthesizes visually plausible nodule textures conditioned on the modulated shape masks. Moreover, we propose to synthesize nodule CXR images by controlling the disentangled nodule attributes for data augmentation, in order to better compensate for the nodules that are easily missed in the detection task. Our experiments demonstrate the enhanced image quality, diversity, and controllability of the proposed lung nodule synthesis framework. We also validate the effectiveness of our data augmentation on greatly improving nodule detection performance.

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Chest X-ray Inpainting with Deep Generative Models

Cited by 2 publications

References 17 publications

Interpreting Black Box Models via Hypothesis Testing

Interpreting Black Box Models via Hypothesis Testing

Image Synthesis with Disentangled Attributes for Chest X-Ray Nodule Augmentation and Detection

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