mlsanityData to 14 Oct 2026

Deepfake Image Generation for Improved Brain Tumor Segmentation

arXiv 2307.14273 ↗First posted 26 Jul 2023eess.IV cs.CV cs.LGcited by 1

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Abstract (arXiv)

As the world progresses in technology and health, awareness of disease by revealing asymptomatic signs improves. It is important to detect and treat tumors in early stage as it can be life-threatening. Computer-aided technologies are used to overcome lingering limitations facing disease diagnosis, while brain tumor segmentation remains a difficult process, especially when multi-modality data is involved. This is mainly attributed to ineffective training due to lack of data and corresponding labelling. This work investigates the feasibility of employing deep-fake image generation for effective brain tumor segmentation. To this end, a Generative Adversarial Network was used for image-to-image translation for increasing dataset size, followed by image segmentation using a U-Net-based convolutional neural network trained with deepfake images. Performance of the proposed approach is compared with ground truth of four publicly available datasets. Results show improved performance in terms of image segmentation quality metrics, and could potentially assist when training with limited data.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 17 of its 36 references that are in the map. First posted on arXiv 26 Jul 2023.

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17 of 36 references.

References · 17

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  3. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,498
  4. 2016Densely Connected Convolutional Networkscited by 6,720
  5. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,527
  6. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,292
  7. 2014How transferable are features in deep neural networks?cited by 1,225
  8. 2017A Survey on Deep Learning in Medical Image Analysiscited by 1,186
  9. 2017Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networkscited by 707
  10. 2017Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentationscited by 620
  11. 2018Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challengecited by 511
  12. 2019Transfusion: Understanding Transfer Learning for Medical Imagingcited by 368
5 more
  1. 2016Medical Image Synthesis with Context-Aware Generative Adversarial Networkscited by 91
  2. 2018TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentationcited by 76
  3. 2020DeepSeg: Deep Neural Network Framework for Automatic Brain Tumor Segmentation using Magnetic Resonance FLAIR Imagescited by 21
  4. 2015Assessment of texture measures susceptibility to noise in conventional and contrast enhanced computed tomography lung tumour imagescited by 1
  5. 2016Susceptibility of texture measures to noise: an application to lung tumor CT imagescited by 1

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  1. 2025Bidirectional Brain Image Translation using Transfer Learning from Generic Pre-trained Modelscited by 0