1 citations · 2 across the 6 of their papers we have counts for
8 papers
Big, Bright, or Invisible: A Frozen-Feature Benchmark of 3D CT Foundation Models
Maulik Chevli, Johannes Brandt, Rickmer Braren +2
Routine CT interpretation is inherently comprehensive, capturing incidental findings across the entire scan volume. 3D CT foundation models could assist this process by providing g…
LungEvaty: A Scalable, Open-Source Transformer-based Deep Learning Model for Lung Cancer Risk Prediction in LDCT Screening
Johannes Brandt, Maulik Chevli, Rickmer Braren +3
Lung cancer risk estimation is gaining increasing importance as more countries introduce population-wide screening programs using low-dose CT (LDCT). As imaging volumes grow, scala…
Leveraging Semantic Triples for Private Document Generation with Local Differential Privacy Guarantees
Stephen Meisenbacher, Maulik Chevli, Florian Matthes
Many works at the intersection of Differential Privacy (DP) in Natural Language Processing aim to protect privacy by transforming texts under DP guarantees. This can be performed i…
On the Impact of Noise in Differentially Private Text Rewriting
Stephen Meisenbacher, Maulik Chevli, Florian Matthes
The field of text privatization often leverages the notion of (DP) to provide formal guarantees in the rewriting or obfuscation of sensitive textual…
A Collocation-based Method for Addressing Challenges in Word-level Metric Differential Privacy
Stephen Meisenbacher, Maulik Chevli, Florian Matthes
Applications of Differential Privacy (DP) in NLP must distinguish between the syntactic level on which a proposed mechanism operates, often taking the form of …
DP-MLM: Differentially Private Text Rewriting Using Masked Language Models
Stephen Meisenbacher, Maulik Chevli, Juraj Vladika +1
The task of text privatization using Differential Privacy has recently taken the form of , in which an input text is obfuscated via the use of generative (…
1-Diffractor: Efficient and Utility-Preserving Text Obfuscation Leveraging Word-Level Metric Differential Privacy
Stephen Meisenbacher, Maulik Chevli, Florian Matthes
The study of privacy-preserving Natural Language Processing (NLP) has gained rising attention in recent years. One promising avenue studies the integration of Differential Privacy…
Privacy-Utility Trade-offs in Neural Networks for Medical Population Graphs: Insights from Differential Privacy and Graph Structure
Tamara T. Mueller, Maulik Chevli, Ameya Daigavane +2
We initiate an empirical investigation into differentially private graph neural networks on population graphs from the medical domain by examining privacy-utility trade-offs at dif…