10 papers · 1 filter
Learning Relative Gene Expression Trends from Pathology Images in Spatial Transcriptomics
Kazuya Nishimura, Haruka Hirose, Ryoma Bise +2
Gene expression estimation from pathology images has the potential to reduce the RNA sequencing cost. Point-wise loss functions have been widely used to minimize the discrepancy be…
Auxiliary Gene Learning: Spatial Gene Expression Estimation by Auxiliary Gene Selection
Kaito Shiku, Kazuya Nishimura, Shinnosuke Matsuo +2
Spatial transcriptomics (ST) is a novel technology that enables the observation of gene expression at the resolution of individual spots within pathological tissues. ST quantifies…
Learning from Majority Label: A Novel Problem in Multi-class Multiple-Instance Learning
Shiku Kaito, Shinnosuke Matsuo, Daiki Suehiro +1
The paper proposes a novel multi-class Multiple-Instance Learning (MIL) problem called Learning from Majority Label (LML). In LML, the majority class of instances in a bag is assig…
NoiseCutMix: A Novel Data Augmentation Approach by Mixing Estimated Noise in Diffusion Models
Shumpei Takezaki, Ryoma Bise, Shinnosuke Matsuo
In this study, we propose a novel data augmentation method that introduces the concept of CutMix into the generation process of diffusion models, thereby exploiting both the abilit…
Adaptive Pseudo Label Selection for Individual Unlabeled Data by Positive and Unlabeled Learning
Takehiro Yamane, Itaru Tsuge, Susumu Saito +1
This paper proposes a novel pseudo-labeling method for medical image segmentation that can perform learning on ``individual images'' to select effective pseudo-labels. We introduce…
Domain Generalization of Pathological Image Segmentation by Patch-Level and WSI-Level Contrastive Learning
Yuki Shigeyasu, Shota Harada, Akihiko Yoshizawa +6
In this paper, we address domain shifts in pathological images by focusing on shifts within whole slide images~(WSIs), such as patient characteristics and tissue thickness, rather…
Enhancing Reliability of Medical Image Diagnosis through Top-rank Learning with Rejection Module
Xiaotong Ji, Ryoma Bise, Seiichi Uchida
In medical image processing, accurate diagnosis is of paramount importance. Leveraging machine learning techniques, particularly top-rank learning, shows significant promise by foc…
Weakly-Supervised Domain Adaptation with Proportion-Constrained Pseudo-Labeling
Takumi Okuo, Shinnosuke Matsuo, Shota Harada +2
Domain shift is a significant challenge in machine learning, particularly in medical applications where data distributions differ across institutions due to variations in data coll…
Towards Spatial Transcriptomics-guided Pathological Image Recognition with Batch-Agnostic Encoder
Kazuya Nishimura, Ryoma Bise, Yasuhiro Kojima
Spatial transcriptomics (ST) is a novel technique that simultaneously captures pathological images and gene expression profiling with spatial coordinates. Since ST is closely relat…
Instance-wise Supervision-level Optimization in Active Learning
Shinnosuke Matsuo, Riku Togashi, Ryoma Bise +2
Active learning (AL) is a label-efficient machine learning paradigm that focuses on selectively annotating high-value instances to maximize learning efficiency. Its effectiveness c…