PDF Viewer

BUILDER'S SANDBOX

Build This Paper

Use an AI coding agent to implement this research.

OpenAI Codex
OpenAI CodexAI Agent

Lightweight coding agent in your terminal.

Claude Code
Claude CodeAI Agent

Agentic coding tool for terminal workflows.

AntiGravity IDE
AntiGravity IDEScaffolding

AI agent mindset installer and workflow scaffolder.

Cursor
CursorIDE

AI-first code editor built on VS Code.

VS Code
VS CodeIDE

Free, open-source editor by Microsoft.

Estimated $9K - $13K over 6-10 weeks.

See exactly what it costs to build this -- with 3 comparable funded startups.

7-day free trial. Cancel anytime.

Discover the researchers behind this paper and find similar experts.

7-day free trial. Cancel anytime.

References (44)

[1]
Resolution scaling governs DINOv3 transfer performance in chest radiograph classification
2025Soroosh Tayebi Arasteh, Mina Shaigan et al.
[2]
MedDINOv3: How to adapt vision foundation models for medical image segmentation?
2025Yuheng Li, Yizhou Wu et al.
[3]
Differential privacy for medical deep learning: methods, tradeoffs, and deployment implications
2025Marziyeh Mohammadi, Mohsen Vejdanihemmat et al.
[4]
Boosting multi-demographic federated learning for chest radiograph analysis using general-purpose self-supervised representations
2025Mahshad Lotfinia, Arash Tayebiarasteh et al.
[5]
The Treasure Trove Hidden in Plain Sight: The Utility of GPT-4 in Chest Radiograph Evaluation.
2024Soroosh Tayebi Arasteh, R. Siepmann et al.
[6]
Differential privacy enables fair and accurate AI-based analysis of speech disorders while protecting patient data
2024Soroosh Tayebi Arasteh, Mahshad Lotfinia et al.
[7]
Reconciling privacy and accuracy in AI for medical imaging
2024Alexander Ziller, Tamara T. Mueller et al.
[8]
Enhancing domain generalization in the AI-based analysis of chest radiographs with federated learning
2023Soroosh Tayebi Arasteh, C. Kuhl et al.
[9]
Enhancing diagnostic deep learning via self-supervised pretraining on large-scale, unlabeled non-medical images
2023Soroosh Tayebi Arasteh, Leo Misera et al.
[10]
DINOv2: Learning Robust Visual Features without Supervision
2023M. Oquab, Timothée Darcet et al.
[11]
Automatic Evaluation of Chest Radiographs – The Data Source Matters, But How Much Exactly?
2023S. Tayebi Arasteh, P. Isfort et al.
[12]
Preserving fairness and diagnostic accuracy in private large-scale AI models for medical imaging
2023Soroosh Tayebi Arasteh, Alexander Ziller et al.
[13]
Artificial Intelligence for Clinical Interpretation of Bedside Chest Radiographs.
2022Firas Khader, T. Han et al.
[14]
Collaborative training of medical artificial intelligence models with non-uniform labels
2022Soroosh Tayebi Arasteh, P. Isfort et al.
[15]
Self-supervised learning in medicine and healthcare
2022R. Krishnan, P. Rajpurkar et al.
[16]
Unlocking High-Accuracy Differentially Private Image Classification through Scale
2022Soham De, Leonard Berrada et al.
[17]
Toward Training at ImageNet Scale with Differential Privacy
2022Alexey Kurakin, Steve Chien et al.
[18]
Reconstructing Training Data with Informed Adversaries
2022Borja Balle, Giovanni Cherubin et al.
[19]
A ConvNet for the 2020s
2022Zhuang Liu, Hanzi Mao et al.
[20]
The Effect of Image Resolution on Automated Classification of Chest X-rays
2021M. Haque, A. Dubey et al.

Showing 20 of 44 references

Founder's Pitch

"Develop privacy-preserving medical image analysis tools using self-supervised pretraining to enhance diagnostic performance."

Medical AIScore: 4View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

1/4 signals

2.5

Quick Build

1/4 signals

2.5

Series A Potential

1/4 signals

2.5

Sources used for this analysis

arXiv Paper

Full-text PDF analysis of the research paper

GitHub Repository

Code availability, stars, and contributor activity

Citation Network

Semantic Scholar citations and co-citation patterns

Community Predictions

Crowd-sourced unicorn probability assessments

Analysis model: GPT-4o · Last scored: 1/27/2026

Explore the full citation network and related research.

7-day free trial. Cancel anytime.

Understand the commercial significance and market impact.

7-day free trial. Cancel anytime.

Get detailed profiles of the research team.

7-day free trial. Cancel anytime.