Titlebook: Deep Generative Models, and Data Augmentation, Labelling, and Imperfections; First Workshop, DGM4 Sandy Engelhardt,Ilkay Oksuz,Yuan Xue Con

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Improved Heatmap-Based Landmark Detectionation of heart function. The location of the prosthesis’ sutures is critical. Obtaining and studying them during the procedure is a valuable learning experience for new surgeons. This paper proposes a landmark detection network for detecting sutures in endoscopic pictures, which solves the problem o

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Cross-Domain Landmarks Detection in Mitral Regurgitationis a complex minimally invasive procedure which is facing the problem of data availability and data privacy. Therefore, the simulation cases are widely used to form surgery training and planning. However, the cross-domain gap may affect the performance significantly as Deep Learning methods rely hea

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Semi-supervised Surgical Tool Detection Based on Highly Confident Pseudo Labeling and Strong Augmentthods heavily rely on the volume of labeled data. However, manually annotating location of tools in surgical videos is quite time-consuming. To overcome this problem, we propose a semi-supervised pipeline for surgical tool detection, using strategies of highly confident pseudo labeling and strong au

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One-Shot Learning for Landmarks Detectionlems but it usually requires a large number of the annotated datasets for the training stage. In addition, traditional methods usually fail for the landmark detection of fine objects. In this paper, we tackle the issue of automatic landmark annotation in 3D volumetric images from a single example ba

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Ultrasound Variational Style Transfer to Generate Images Beyond the Observed Domain latent space to generate images from a broader domain than what was observed. We show that using our generative approach for ultrasound data augmentation and domain adaptation during training improves the performance of the resulting deep learning models, even when tested within the observed domain.

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https://doi.org/10.1007/978-3-662-43839-8e variables. We conduct extensive qualitative and quantitative assessments on two publicly available medical imaging datasets (LIDC and HAM10000) and test for conditional image generation and style-content disentanglement. We also show that our proposed model (DRAI) achieves the best disentanglement score and has the best overall performance.

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https://doi.org/10.1007/978-3-662-43839-8y distributions within each masked region using a novel Variational Autoencoder (VAE) based hierarchical probabilistic network. Our approach then generates a diverse set of inpainted images, all of which appear visually appropriate.