6 citations · 7 across the 15 of their papers we have counts for
5 papers · 1 filter
Selective Inference for Deep Clustering in Latent Spaces
Eina Mizui, Tomohiro Shiraishi, Shunichi Nishino +1
Deep clustering is a powerful approach for discovering meaningful structures in high-dimensional data by learning a low-dimensional latent representation prior to clustering. Despi…
Statistical Testing for Multiple Instance Learning via Selective Inference with Applications to Computational Pathology
Noriaki Hashimoto, Shuichi Nishino, Teruyuki Katsuoka +7
Multiple instance learning (MIL) is widely used in computational pathology because it enables weakly supervised analysis of whole-slide images (WSIs) without requiring patch-level…
Automatic Statistical Test for Rationally Expressible Algorithms by Selective Inference, with Applications to Feature Selection
Teruyuki Katsuoka, Tomohiro Shiraishi, Shuichi Nishino +1
Selective inference (SI) provides statistically valid -values for hypotheses selected by applying an algorithm to the data, correcting for the bias that arises when the same dat…
Post-ADC Inference: Valid Inference After Active Data Collection
Shuichi Nishino, Tomohiro Shiraishi, Teruyuki Katsuoka +1
The validity of statistical inference depends critically on how data are collected. When data gathered through active data collection (ADC) are reused for a post-hoc inferential ta…
Statistical Testing Framework for Clustering Pipelines by Selective Inference
Yugo Miyata, Tomohiro Shiraishi, Shuichi Nishino +1
A data analysis pipeline is a structured sequence of steps that transforms raw data into meaningful insights by integrating multiple analysis algorithms. In many practical applicat…