arXiv subjects

Sangheum Hwang

Publications and source records attributed to Sangheum Hwang.

23 records · Page 2Linked to original sources

arXiv2017

A Unified Framework for Tumor Proliferation Score Prediction in Breast Histopathology

We present a unified framework to predict tumor proliferation scores from breast histopathology whole slide images. Our system offers a fully automated solution to predicting both a molecular data-based, and a mitosis counting-based tumor proliferation score. The framework integrates three modules, each fine-tuned to maximize the overall performance: An image processing component for handling whole slide images, a deep learning based mitosis detection network, and a proliferation scores prediction module. We have achieved 0.567 quadratic weighted Cohen's kappa in mitosis counting-based score prediction and 0.652 F1-score in mitosis detection. On Spearman's correlation coefficient, which evaluates predictive accuracy on the molecular data based score, the system obtained 0.6171. Our approach won first place in all of the three tasks in Tumor Proliferation Assessment Challenge 2016 which is MICCAI grand challenge.

cs.CV↗

arXiv2017

Accurate Lung Segmentation via Network-Wise Training of Convolutional Networks

We introduce an accurate lung segmentation model for chest radiographs based on deep convolutional neural networks. Our model is based on atrous convolutional layers to increase the field-of-view of filters efficiently. To improve segmentation performances further, we also propose a multi-stage training strategy, network-wise training, which the current stage network is fed with both input images and the outputs from pre-stage network. It is shown that this strategy has an ability to reduce falsely predicted labels and produce smooth boundaries of lung fields. We evaluate the proposed model on a common benchmark dataset, JSRT, and achieve the state-of-the-art segmentation performances with much fewer model parameters.

cs.CV↗

arXiv2016

Semantic Noise Modeling for Better Representation Learning

Latent representation learned from multi-layered neural networks via hierarchical feature abstraction enables recent success of deep learning. Under the deep learning framework, generalization performance highly depends on the learned latent representation which is obtained from an appropriate training scenario with a task-specific objective on a designed network model. In this work, we propose a novel latent space modeling method to learn better latent representation. We designed a neural network model based on the assumption that good base representation can be attained by maximizing the total correlation between the input, latent, and output variables. From the base model, we introduce a semantic noise modeling method which enables class-conditional perturbation on latent space to enhance the representational power of learned latent feature. During training, latent vector representation can be stochastically perturbed by a modeled class-conditional additive noise while maintaining its original semantic feature. It implicitly brings the effect of semantic augmentation on the latent space. The proposed model can be easily learned by back-propagation with common gradient-based optimization algorithms. Experimental results show that the proposed method helps to achieve performance benefits against various previous approaches. We also provide the empirical analyses for the proposed class-conditional perturbation process including t-SNE visualization.

cs.LG↗

arXiv2016

Deconvolutional Feature Stacking for Weakly-Supervised Semantic Segmentation

A weakly-supervised semantic segmentation framework with a tied deconvolutional neural network is presented. Each deconvolution layer in the framework consists of unpooling and deconvolution operations. 'Unpooling' upsamples the input feature map based on unpooling switches defined by corresponding convolution layer's pooling operation. 'Deconvolution' convolves the input unpooled features by using convolutional weights tied with the corresponding convolution layer's convolution operation. The unpooling-deconvolution combination helps to eliminate less discriminative features in a feature extraction stage, since output features of the deconvolution layer are reconstructed from the most discriminative unpooled features instead of the raw one. This results in reduction of false positives in a pixel-level inference stage. All the feature maps restored from the entire deconvolution layers can constitute a rich discriminative feature set according to different abstraction levels. Those features are stacked to be selectively used for generating class-specific activation maps. Under the weak supervision (image-level labels), the proposed framework shows promising results on lesion segmentation in medical images (chest X-rays) and achieves state-of-the-art performance on the PASCAL VOC segmentation dataset in the same experimental condition.

cs.CV↗

arXiv2016

Self-Transfer Learning for Fully Weakly Supervised Object Localization

Recent advances of deep learning have achieved remarkable performances in various challenging computer vision tasks. Especially in object localization, deep convolutional neural networks outperform traditional approaches based on extraction of data/task-driven features instead of hand-crafted features. Although location information of region-of-interests (ROIs) gives good prior for object localization, it requires heavy annotation efforts from human resources. Thus a weakly supervised framework for object localization is introduced. The term "weakly" means that this framework only uses image-level labeled datasets to train a network. With the help of transfer learning which adopts weight parameters of a pre-trained network, the weakly supervised learning framework for object localization performs well because the pre-trained network already has well-trained class-specific features. However, those approaches cannot be used for some applications which do not have pre-trained networks or well-localized large scale images. Medical image analysis is a representative among those applications because it is impossible to obtain such pre-trained networks. In this work, we present a "fully" weakly supervised framework for object localization ("semi"-weakly is the counterpart which uses pre-trained filters for weakly supervised localization) named as self-transfer learning (STL). It jointly optimizes both classification and localization networks simultaneously. By controlling a supervision level of the localization network, STL helps the localization network focus on correct ROIs without any types of priors. We evaluate the proposed STL framework using two medical image datasets, chest X-rays and mammograms, and achieve signiticantly better localization performance compared to previous weakly supervised approaches.

cs.CV↗