Tootfinder

Robust Pan-Cancer Mitotic Figure Detection with YOLOv12
Rapha\"el Bourgade, Guillaume Balezo, Thomas Walter
https://arxiv.org/abs/2509.02593 https://a…

Robust Pan-Cancer Mitotic Figure Detection with YOLOv12
Mitotic figures represent a key histoprognostic feature in tumor pathology, providing crucial insights into tumor aggressiveness and proliferation. However, their identification remains challenging, subject to significant inter-observer variability, even among experienced pathologists. To address this issue, the MItosis DOmain Generalization (MIDOG) 2025 challenge marks the third edition of an international competition aiming to develop robust mitosis detection algorithms. In this paper, we pre…

Nonparametric Bounds in Causal Mediation Analysis in the Presence of Unmeasured Confounding and Imperfect Compliance
Wei Liang, Changbao Wu
https://arxiv.org/abs/2509.02756 http…

Nonparametric Bounds in Causal Mediation Analysis in the Presence of Unmeasured Confounding and Imperfect Compliance
The average causal mediation effect (ACME) and the natural direct effect (NDE) are two parameters of primary interest in causal mediation analysis. However, the two causal parameters are not identifiable from randomized experimental data in the presence of outcome-mediator confounding and treatment-assignment noncompliance. Sjölander (2009) addressed the partial identification issue and derived nonparametric bounds of the NDE in randomized controlled trials under a set of monotonicity assumpti…

ICR: Iterative Clarification and Rewriting for Conversational Search
Zhiyu Cao, Peifeng Li, Qiaoming Zhu
https://arxiv.org/abs/2509.05100 https://arxiv.org…

ICR: Iterative Clarification and Rewriting for Conversational Search
Most previous work on Conversational Query Rewriting employs an end-to-end rewriting paradigm. However, this approach is hindered by the issue of multiple fuzzy expressions within the query, which complicates the simultaneous identification and rewriting of multiple positions. To address this issue, we propose a novel framework ICR (Iterative Clarification and Rewriting), an iterative rewriting scheme that pivots on clarification questions. Within this framework, the model alternates between ge…

Pothole Detection and Recognition based on Transfer Learning
Mang Hu, Qianqian Xia
https://arxiv.org/abs/2509.06750 https://arxiv.org/pdf/2509.06750…

Pothole Detection and Recognition based on Transfer Learning
With the rapid development of computer vision and machine learning, automated methods for pothole detection and recognition based on image and video data have received significant attention. It is of great significance for social development to conduct an in-depth analysis of road images through feature extraction, thereby achieving automatic identification of the pothole condition in new images. Consequently, this is the main issue addressed in this study. Based on preprocessing techniques suc…

Extension of Spatial k-Anonymity: New Metrics for Assessing the Anonymity of Geomasked Data Considering Realistic Attack Scenarios
Simon Cremer, Lydia Jehmlich, Rainer Lenz
https://arxiv.org/abs/2509.07505

Extension of Spatial k-Anonymity: New Metrics for Assessing the Anonymity of Geomasked Data Considering Realistic Attack Scenarios
Spatial data are gaining increasing importance in many areas of research. Particularly spatial health data are becoming increasingly important for medical research, for example, to better understand relationships between environmental factors and disease patterns. However, their use is often restricted by legal data protection regulations, since georeferenced personal information carries a high risk of re-identification of individuals. To address this issue, what are called geomasking methods a…

Denoised IPW-Lasso for Heterogeneous Treatment Effect Estimation in Randomized Experiments
Mingqian Guan, Komei Fujita, Naoya Sueishi, Shota Yasui
https://arxiv.org/abs/2510.10527

Denoised IPW-Lasso for Heterogeneous Treatment Effect Estimation in Randomized Experiments
This paper proposes a new method for estimating conditional average treatment effects (CATE) in randomized experiments. We adopt inverse probability weighting (IPW) for identification; however, IPW-transformed outcomes are known to be noisy, even when true propensity scores are used. To address this issue, we introduce a noise reduction procedure and estimate a linear CATE model using Lasso, achieving both accuracy and interpretability. We theoretically show that denoising reduces the predictio…