GAN-Based Synthetic Gastrointestinal Image Generation

Adjei, Prince Ebenezer; Lonseko, Zenebe Markos; Rao, Nini

doi:10.1109/iccwamtip51612.2020.9317341

Cited by 3 publications

(2 citation statements)

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“…Thus, by reducing the need for annotating large volumes of medical images, an efficient SSL framework offers an attractive alternative to supervised DL approaches in automatic segmentation applications in biomedical imaging. To effectively validate the quality of a SSL method, a few studies [ 19 , 20 , 21 ] have applied a generative adversarial network (GAN) [ 18 ]. GANs comprise a generator network and a discriminator network [ 18 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

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Semi-Supervised Segmentation Framework for Gastrointestinal Lesion Diagnosis in Endoscopic Images

Lonseko

Adjei

et al. 2023

JPM

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Background: Accurate gastrointestinal (GI) lesion segmentation is crucial in diagnosing digestive tract diseases. An automatic lesion segmentation in endoscopic images is vital to relieving physicians’ burden and improving the survival rate of patients. However, pixel-wise annotations are highly intensive, especially in clinical settings, while numerous unlabeled image datasets could be available, although the significant results of deep learning approaches in several tasks heavily depend on large labeled datasets. Limited labeled data also hinder trained models’ generalizability under fully supervised learning for computer-aided diagnosis (CAD) systems. Methods: This work proposes a generative adversarial learning-based semi-supervised segmentation framework for GI lesion diagnosis in endoscopic images to tackle the challenge of limited annotations. The proposed approach leverages limited annotated and large unlabeled datasets in the training networks. We extensively tested the proposed method on 4880 endoscopic images. Results: Compared with current related works, the proposed method validates better results (Dice similarity coefficient = 89.42 ± 3.92, Intersection over union = 80.04 ± 5.75, Precision = 91.72 ± 4.05, Recall = 90.11 ± 5.64, and Hausdorff distance = 23.28 ± 14.36) on the challenging multi-sited datasets, confirming the effectiveness of the proposed framework. Conclusion: We explore a semi-supervised lesion segmentation method to employ the full use of multiple unlabeled endoscopic images to improve lesion segmentation accuracy. Experimental results confirmed the potential of our method and outperformed promising results compared with the current related works. The proposed CAD system can minimize diagnostic errors.

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Section: Introductionmentioning

confidence: 99%

“…In this work, we propose a robust SSL framework for training deep models with small labeled training samples. Furthermore, we utilize adversarial generative modeling similar to [ 23 ] for GI lesion segmentation tasks [ 21 ].…”

Section: Introductionmentioning

confidence: 99%