Learning Fixed Points in Generative Adversarial Networks: From Image-to-Image Translation to Disease Detection and Localization (1908.06965v2)

Abstract: Generative adversarial networks (GANs) have ushered in a revolution in image-to-image translation. The development and proliferation of GANs raises an interesting question: can we train a GAN to remove an object, if present, from an image while otherwise preserving the image? Specifically, can a GAN "virtually heal" anyone by turning his medical image, with an unknown health status (diseased or healthy), into a healthy one, so that diseased regions could be revealed by subtracting those two images? Such a task requires a GAN to identify a minimal subset of target pixels for domain translation, an ability that we call fixed-point translation, which no GAN is equipped with yet. Therefore, we propose a new GAN, called Fixed-Point GAN, trained by (1) supervising same-domain translation through a conditional identity loss, and (2) regularizing cross-domain translation through revised adversarial, domain classification, and cycle consistency loss. Based on fixed-point translation, we further derive a novel framework for disease detection and localization using only image-level annotation. Qualitative and quantitative evaluations demonstrate that the proposed method outperforms the state of the art in multi-domain image-to-image translation and that it surpasses predominant weakly-supervised localization methods in both disease detection and localization. Implementation is available at https://github.com/jlianglab/Fixed-Point-GAN.

• The paper introduces Fixed-Point GAN, employing a fixed-point translation method to retain image features by applying minimal modifications.
• It utilizes a dual learning strategy with conditional identity loss to achieve precise same-domain and cross-domain translations, significantly reducing reconstruction errors compared to traditional GANs.
• Empirical results demonstrate enhanced disease detection and lesion localization on benchmarks like BRATS 2013, underscoring its potential for critical medical diagnostics.

An Analytical Perspective on Fixed-Point GAN for Image Translation and Disease Localization

This paper introduces an innovative approach in the domain of Generative Adversarial Networks (GANs), coined as Fixed-Point GAN, designed primarily to overcome limitations observed in existing image-to-image translation frameworks, particularly in tasks involving disease detection and localization. The authors propose a paradigm shift in training GANs by introducing the concept of fixed-point translation. The methodology and rigorous empirical analyses underscore the utility of this approach in both synthetic and real-world domains, encompassing tasks from cosmetic image modifications to critical medical diagnostic applications.

Fixed-Point Translation in GANs

Fixed-Point GAN distinguishes itself by incorporating a dual learning strategy, optimizing both cross-domain and same-domain translations. Traditional frameworks, such as CycleGAN and StarGAN, lack this nuanced approach, thereby imposing unnecessary alterations in generated outputs. This is particularly detrimental in medically sensitive applications where alterations may lead to misdiagnoses. The proposed GAN architecture, therefore, is architected to adeptly preserve image fidelity, applying minimal necessary transformations.

The training of the Fixed-Point GAN is novel in its use of a conditional identity loss, which ensures that same-domain translations yield outputs that closely match the inputs, effectively learning to make no change unless absolutely required. Coupled with regularization mechanics applied to cross-domain translations, this configuration pivots the generator towards minimal and precise transformations, avoiding superfluous alterations that could compromise critical image regions, especially in medical imaging contexts.

Empirical Results and Comparative Analysis

Evaluation on the CelebFaces Attributes dataset demonstrates the GAN's superiority in retaining background and subject details while performing desired attribute transformations. Quantitative metrics, such as L1 loss comparisons, vividly illustrate Fixed-Point GAN's ability to minimize reconstruction errors compared to StarGAN, marking a significant advancement in achieving high-fidelity image translation.

In tasks of disease detection, the authors employ Fixed-Point GAN within the BRATS 2013 dataset, focusing on brain lesion detection, and a pulmonary embolism dataset. The model shows substantial improvements in lesion localization and image-level detection tasks over conventional GANs and anomaly detection benchmarks (f-AnoGAN and Alex's GAN). The improved sensitivity rates at reduced false positive levels highlight the method's robustness and potential for clinical deployment.

Implications and Future Directions

The Fixed-Point GAN fosters impactful implications, particularly in medical diagnostics, where image translation can serve as a proxy for identifying pathology by effectively "removing" anomalies. This GAN's capacity to handle unpaired images, obviating the need for transformation-specific paired datasets, aligns well with practical constraints in acquiring balanced medical image repositories.

The methodological innovations in fixed-point translation advocate a broader applicability spectrum beyond medical imaging into any domain where image integrity needs preservation amidst transformations. Future research might further this approach, embedding such GANs into real-time diagnostic systems, enhancing the scale and scope of automated detection frameworks, and potentially incorporating advances in self-supervised learning paradigms to further minimize annotation dependencies.

In conclusion, the introduction of Fixed-Point GAN sets a foundational framework for advanced image translation tasks, mitigating traditional GAN limitations and opening avenues for robust, minimally invasive transformations in image-centric diagnostics and beyond.