• Corpus ID: 238198605

3N-GAN: Semi-Supervised Classification of X-Ray Images with a 3-Player Adversarial Framework

  title={3N-GAN: Semi-Supervised Classification of X-Ray Images with a 3-Player Adversarial Framework},
  author={Shafinul Haque and Ayaan Haque},
The success of deep learning for medical imaging tasks, such as classification, is heavily reliant on the availability of large-scale datasets. However, acquiring datasets with large quantities of labeled data is challenging, as labeling is expensive and time-consuming. Semi-supervised learning (SSL) is a growing alternative to fully-supervised learning, but requires unlabeled samples for training. In medical imaging, many datasets lack unlabeled data entirely, so SSL can’t be conventionally… 

Figures from this paper



EC-GAN: Low-Sample Classification using Semi-Supervised Algorithms and GANs

This work proposes a novel GAN model namely External Classifier GAN (EC-GAN), that utilizes GANs and semi-supervised algorithms to improve classification in fully-super supervised regimes, and leverages a GAN to generate artificial data used to supplement supervised classification.

Improved Techniques for Training GANs

This work focuses on two applications of GANs: semi-supervised learning, and the generation of images that humans find visually realistic, and presents ImageNet samples with unprecedented resolution and shows that the methods enable the model to learn recognizable features of ImageNet classes.

Semi-Supervised Medical Image Classification With Relation-Driven Self-Ensembling Model

A novel sample relation consistency (SRC) paradigm to effectively exploit unlabeled data by modeling the relationship information among different samples, which outperforms many state-of-the-art semi-supervised learning methods on both single-label and multi-label image classification scenarios.

Multimix: Sparingly-Supervised, Extreme Multitask Learning from Medical Images

This work proposes MultiMix, a novel multitask learning model that jointly learns disease classification and anatomical segmentation in a sparingly supervised manner, while preserving explainability through bridge saliency between the two tasks.

Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

This work introduces a class of CNNs called deep convolutional generative adversarial networks (DCGANs), that have certain architectural constraints, and demonstrates that they are a strong candidate for unsupervised learning.

FaceID-GAN: Learning a Symmetry Three-Player GAN for Identity-Preserving Face Synthesis

FaceID-GAN is able to generate faces of arbitrary viewpoint while preserve identity, outperforming recent advanced approaches and substantially alleviating training difficulty of GAN.

DeepFakes: a New Threat to Face Recognition? Assessment and Detection

This paper presents the first publicly available set of Deepfake videos generated from videos of VidTIMIT database, and demonstrates that GAN-generated Deep fake videos are challenging for both face recognition systems and existing detection methods.

CE-Net: Context Encoder Network for 2D Medical Image Segmentation

Comprehensive results show that the proposed CE-Net method outperforms the original U- net method and other state-of-the-art methods for optic disc segmentation, vessel detection, lung segmentation , cell contour segmentation and retinal optical coherence tomography layer segmentation.

Medical Image Analysis using Convolutional Neural Networks: A Review

A comprehensive review of the current state-of-the-art in medical image analysis using deep convolutional networks is presented and the challenges and potential of these techniques are also highlighted.

Pseudo-Label : The Simple and Efficient Semi-Supervised Learning Method for Deep Neural Networks

This simple and efficient method of semi-supervised learning for deep neural networks is proposed, trained in a supervised fashion with labeled and unlabeled data simultaneously and favors a low-density separation between classes.