31 citations · 83 across the 16 of their papers we have counts for
18 papers
Deep Bayesian Active Learning-to-Rank with Relative Annotation for Estimation of Ulcerative Colitis Severity
Takeaki Kadota, Hideaki Hayashi, Ryoma Bise +2
Automatic image-based severity estimation is an important task in computer-aided diagnosis. Severity estimation by deep learning requires a large amount of training data to achieve…
Pseudo-label Learning with Calibrated Confidence Using an Energy-based Model
Masahito Toba, Seiichi Uchida, Hideaki Hayashi
In pseudo-labeling (PL), which is a type of semi-supervised learning, pseudo-labels are assigned based on the confidence scores provided by the classifier; therefore, accurate conf…
Analyzing Font Style Usage and Contextual Factors in Real Images
Naoya Yasukochi, Hideaki Hayashi, Daichi Haraguchi +1
There are various font styles in the world. Different styles give different impressions and readability. This paper analyzes the relationship between font styles and contextual fac…
A Hybrid of Generative and Discriminative Models Based on the Gaussian-coupled Softmax Layer
Hideaki Hayashi
Generative models have advantageous characteristics for classification tasks such as the availability of unsupervised data and calibrated confidence, whereas discriminative models…
Deep Bayesian Active-Learning-to-Rank for Endoscopic Image Data
Takeaki Kadota, Hideaki Hayashi, Ryoma Bise +2
Automatic image-based disease severity estimation generally uses discrete (i.e., quantized) severity labels. Annotating discrete labels is often difficult due to the images with am…
Order-Guided Disentangled Representation Learning for Ulcerative Colitis Classification with Limited Labels
Shota Harada, Ryoma Bise, Hideaki Hayashi +2
Ulcerative colitis (UC) classification, which is an important task for endoscopic diagnosis, involves two main difficulties. First, endoscopic images with the annotation about UC (…
Meta-learning of Pooling Layers for Character Recognition
Takato Otsuzuki, Heon Song, Seiichi Uchida +1
In convolutional neural network-based character recognition, pooling layers play an important role in dimensionality reduction and deformation compensation. However, their kernel s…
Layer-Wise Interpretation of Deep Neural Networks Using Identity Initialization
Shohei Kubota, Hideaki Hayashi, Tomohiro Hayase +1
The interpretability of neural networks (NNs) is a challenging but essential topic for transparency in the decision-making process using machine learning. One of the reasons for th…
Handwriting Prediction Considering Inter-Class Bifurcation Structures
Masaki Yamagata, Hideaki Hayashi, Seiichi Uchida
Temporal prediction is a still difficult task due to the chaotic behavior, non-Markovian characteristics, and non-stationary noise of temporal signals. Handwriting prediction is al…
Regularized Pooling
Takato Otsuzuki, Hideaki Hayashi, Yuchen Zheng +1
In convolutional neural networks (CNNs), pooling operations play important roles such as dimensionality reduction and deformation compensation. In general, max pooling, which is th…
A Discriminative Gaussian Mixture Model with Sparsity
Hideaki Hayashi, Seiichi Uchida
In probabilistic classification, a discriminative model based on the softmax function has a potential limitation in that it assumes unimodality for each class in the feature space.…
Modality Conversion of Handwritten Patterns by Cross Variational Autoencoders
Taichi Sumi, Brian Kenji Iwana, Hideaki Hayashi +1
This research attempts to construct a network that can convert online and offline handwritten characters to each other. The proposed network consists of two Variational Auto-Encode…
A Trainable Multiplication Layer for Auto-correlation and Co-occurrence Extraction
Hideaki Hayashi, Seiichi Uchida
In this paper, we propose a trainable multiplication layer (TML) for a neural network that can be used to calculate the multiplication between the input features. Taking an image a…
GlyphGAN: Style-Consistent Font Generation Based on Generative Adversarial Networks
Hideaki Hayashi, Kohtaro Abe, Seiichi Uchida
In this paper, we propose GlyphGAN: style-consistent font generation based on generative adversarial networks (GANs). GANs are a framework for learning a generative model using a s…
Biosignal Generation and Latent Variable Analysis with Recurrent Generative Adversarial Networks
Shota Harada, Hideaki Hayashi, Seiichi Uchida
The effectiveness of biosignal generation and data augmentation with biosignal generative models based on generative adversarial networks (GANs), which are a type of deep learning…
Combining Noise-to-Image and Image-to-Image GANs: Brain MR Image Augmentation for Tumor Detection
Changhee Han, Leonardo Rundo, Ryosuke Araki +5
Convolutional Neural Networks (CNNs) achieve excellent computer-assisted diagnosis with sufficient annotated training data. However, most medical imaging datasets are small and fra…
ProbAct: A Probabilistic Activation Function for Deep Neural Networks
Kumar Shridhar, Joonho Lee, Hideaki Hayashi +6
Activation functions play an important role in training artificial neural networks. The majority of currently used activation functions are deterministic in nature, with their fixe…
Infinite Brain MR Images: PGGAN-based Data Augmentation for Tumor Detection
Changhee Han, Leonardo Rundo, Ryosuke Araki +4
Due to the lack of available annotated medical images, accurate computer-assisted diagnosis requires intensive Data Augmentation (DA) techniques, such as geometric/intensity transf…