1 citations · 1 across the 6 of their papers we have counts for
7 papers
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…
si4onnx: A Python package for Selective Inference in Deep Learning Models
Teruyuki Katsuoka, Tomohiro Shiraishi, Daiki Miwa +2
In this paper, we introduce si4onnx, a package for performing selective inference on deep learning models. Techniques such as CAM in XAI and reconstruction-based anomaly detection…
Statistical Test for Diffusion-Based Anomaly Localization via Selective Inference
Teruyuki Katsuoka, Tomohiro Shiraishi, Daiki Miwa +2
Anomaly localization in images -- identifying regions that deviate from normal patterns -- is vital in applications such as medical diagnosis and industrial inspection. A recent tr…
Statistical Test for Anomaly Detections by Variational Auto-Encoders
Daiki Miwa, Tomohiro Shiraishi, Vo Nguyen Le Duy +2
In this study, we consider the reliability assessment of anomaly detection (AD) using Variational Autoencoder (VAE). Over the last decade, VAE-based AD has been actively studied in…
Statistical Test for Attention Map in Vision Transformer
Tomohiro Shiraishi, Daiki Miwa, Teruyuki Katsuoka +3
The Vision Transformer (ViT) demonstrates exceptional performance in various computer vision tasks. Attention is crucial for ViT to capture complex wide-ranging relationships among…
Selective Inference for Changepoint detection by Recurrent Neural Network
Tomohiro Shiraishi, Daiki Miwa, Vo Nguyen Le Duy +1
In this study, we investigate the quantification of the statistical reliability of detected change points (CPs) in time series using a Recurrent Neural Network (RNN). Thanks to its…
Bounded P-values in Parametric Programming-based Selective Inference
Tomohiro Shiraishi, Daiki Miwa, Vo Nguyen Le Duy +1
Selective inference (SI) has been actively studied as a promising framework for statistical hypothesis testing for data-driven hypotheses. The basic idea of SI is to make inference…