6 papers
Hypernetwork-Based Adaptive Aggregation for Multimodal Multiple-Instance Learning in Predicting Coronary Calcium Debulking
Kaito Shiku, Ichika Seo, Tetsuya Matoba +3
In this paper, we present the first attempt to estimate the necessity of debulking coronary artery calcifications from computed tomography (CT) images. We formulate this task as a…
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…
Domain Adaptation for Ulcerative Colitis Severity Estimation Using Patient-Level Diagnoses
Takamasa Yamaguchi, Brian Kenji Iwana, Ryoma Bise +4
The development of methods to estimate the severity of Ulcerative Colitis (UC) is of significant importance. However, these methods often suffer from domain shifts caused by differ…
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…
Ordinal Multiple-instance Learning for Ulcerative Colitis Severity Estimation with Selective Aggregated Transformer
Kaito Shiku, Kazuya Nishimura, Daiki Suehiro +2
Patient-level diagnosis of severity in ulcerative colitis (UC) is common in real clinical settings, where the most severe score in a patient is recorded. However, previous UC class…