most citedA generalizable 3D framework and model for self-supervised learning in medical imaging

13 citations

6 papers

stat.ML2026

Gradient-Guided Furthest Point Sampling for Robust Training Set Selection

Morris Trestman, Stefan Gugler, Felix A. Faber +1

Training set sampling methods are used to improve model performance and lower data costs in machine learning problems relevant to chemistry. We introduce Gradient Guided Furthest P…

astro-ph.GA2026

Integral Field Unit Spectroscopy with One Fiber

Zehao Peng, Biprateep Dey, Chris J. Maddison +1

Integral field unit (IFU) spectroscopy provides spatially resolved spectra across galaxies, offering crucial insights into their evolution. However, its high observational cost lim…

astro-ph.GA2026

Characterizing Stellar Streams with Error-Aware Machine Learning

Alexandros Pratsos, Biprateep Dey, Ting S. Li

Stellar streams are thin, elongated collections of stars formed by gravitational disruption of orbiting star clusters or dwarf galaxies and are highly sensitive probes of the Milky…

eess.IV202613 cited

A generalizable 3D framework and model for self-supervised learning in medical imaging

Tony Xu, Sepehr Hosseini, Chris Anderson +4

Current self-supervised learning methods for 3D medical imaging rely on simple pretext formulations and organ- or modality-specific datasets, limiting their generalizability and sc…

cs.LG2026

Crowding Out The Noise: Algorithmic Collective Action Under Differential Privacy

Rushabh Solanki, Meghana Bhange, Ulrich Aïvodji +1

The integration of AI into daily life has generated considerable attention and excitement, while also raising concerns about automating algorithmic harms and re-entrenching existin…

quant-ph2026

Design Principles for Enhanced Quantum Transport with Site-Dependent Noise

Maggie Lawrence, Elise Wang, Dvira Segal

Environmental noise can enhance transport, an effect known as environmental noise-assisted quantum transport. Most theoretical studies focus on optimizing system parameters under s…