18 citations · 33 across the 10 of their papers we have counts for
10 papers
Adaptive Block Sparse Regularization under Arbitrary Linear Transform
Takanobu Furuhashi, Hidekata Hontani, Tatsuya Yokota
We propose a convex and fast signal reconstruction method for block sparsity under arbitrary linear transform with unknown block structure. The proposed method is a generalization…
ADMM-MM Algorithm for General Tensor Decomposition
Manabu Mukai, Hidekata Hontani, Tatsuya Yokota
In this paper, we propose a new unified optimization algorithm for general tensor decomposition which is formulated as an inverse problem for low-rank tensors in the general linear…
Transformer-based Personalized Attention Mechanism for Medical Images with Clinical Records
Yusuke Takagi, Noriaki Hashimoto, Hiroki Masuda +4
In medical image diagnosis, identifying the attention region, i.e., the region of interest for which the diagnosis is made, is an important task. Various methods have been develope…
Manifold Modeling in Quotient Space: Learning An Invariant Mapping with Decodability of Image Patches
Tatsuya Yokota, Hidekata Hontani
This study proposes a framework for manifold learning of image patches using the concept of equivalence classes: manifold modeling in quotient space (MMQS). In MMQS, we do not cons…
Case-based Similar Image Retrieval for Weakly Annotated Large Histopathological Images of Malignant Lymphoma Using Deep Metric Learning
Noriaki Hashimoto, Yusuke Takagi, Hiroki Masuda +20
In the present study, we propose a novel case-based similar image retrieval (SIR) method for hematoxylin and eosin (H&E)-stained histopathological images of malignant lymphoma. Whe…
Multi-scale Domain-adversarial Multiple-instance CNN for Cancer Subtype Classification with Unannotated Histopathological Images
Noriaki Hashimoto, Daisuke Fukushima, Ryoichi Koga +7
We propose a new method for cancer subtype classification from histopathological images, which can automatically detect tumor-specific features in a given whole slide image (WSI).…
Manifold Modeling in Embedded Space: A Perspective for Interpreting Deep Image Prior
Tatsuya Yokota, Hidekata Hontani, Qibin Zhao +1
Deep image prior (DIP), which utilizes a deep convolutional network (ConvNet) structure itself as an image prior, has attracted attentions in computer vision and machine learning c…
Computing Valid p-values for Image Segmentation by Selective Inference
Kosuke Tanizaki, Noriaki Hashimoto, Yu Inatsu +2
Image segmentation is one of the most fundamental tasks of computer vision. In many practical applications, it is essential to properly evaluate the reliability of individual segme…
Missing Slice Recovery for Tensors Using a Low-rank Model in Embedded Space
Tatsuya Yokota, Burak Erem, Seyhmus Guler +2
Let us consider a case where all of the elements in some continuous slices are missing in tensor data. In this case, the nuclear-norm and total variation regularization methods usu…
Simultaneous Tensor Completion and Denoising by Noise Inequality Constrained Convex Optimization
Tatsuya Yokota, Hidekata Hontani
Tensor completion is a technique of filling missing elements of the incomplete data tensors. It being actively studied based on the convex optimization scheme such as nuclear-norm…