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Found 7 papers, 0 papers with code
Self-Supervised CycleGAN for Object-Preserving Image-to-Image Domain Adaptation
Recent generative adversarial network (GAN) based methods (e. g., CycleGAN) are prone to fail at preserving image-objects in image-to-image translation, which reduces their practicality on tasks such as domain adaptation.
Instance-aware Self-supervised Learning for Nuclei Segmentation
Due to the wide existence and large morphological variances of nuclei, accurate nuclei instance segmentation is still one of the most challenging tasks in computational pathology.
MI^2GAN: Generative Adversarial Network for Medical Image Domain Adaptation using Mutual Information Constraint
Domain shift between medical images from multicentres is still an open question for the community, which degrades the generalization performance of deep learning models.
Self-Loop Uncertainty: A Novel Pseudo-Label for Semi-Supervised Medical Image Segmentation
A novel pseudo-label (namely self-loop uncertainty), generated by recurrently optimizing the neural network with a self-supervised task, is adopted as the ground-truth for the unlabeled images to augment the training set and boost the segmentation accuracy.
Texture Deformation Based Generative Adversarial Networks for Face Editing
We also evaluate our approach qualitatively and quantitatively on facial attribute and facial expression synthesis.
Reversed Active Learning based Atrous DenseNet for Pathological Image Classification
However, the usage of deep learning networks for the pathological image analysis encounters several challenges, e. g. high resolution (gigapixel) of pathological images and lack of annotations of cancer areas.
Active Learning for Breast Cancer Identification
Our RAL is applied to the training set of a simple convolutional neural network (CNN) to remove mislabeled images.
