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The Effect of Intrinsic Dataset Properties on Generalization: Unraveling
  Learning Differences Between Natural and Medical Images
v1v2v3 (latest)

The Effect of Intrinsic Dataset Properties on Generalization: Unraveling Learning Differences Between Natural and Medical Images

16 January 2024
Nicholas Konz
Maciej A. Mazurowski
ArXiv (abs)PDFHTMLGithub (14★)

Papers citing "The Effect of Intrinsic Dataset Properties on Generalization: Unraveling Learning Differences Between Natural and Medical Images"

6 / 6 papers shown
Title
Adjustment for Confounding using Pre-Trained Representations
Adjustment for Confounding using Pre-Trained Representations
Rickmer Schulte
David Rügamer
Thomas Nagler
CMLBDL
27
0
0
17 Jun 2025
Physical foundations for trustworthy medical imaging: a review for artificial intelligence researchers
Physical foundations for trustworthy medical imaging: a review for artificial intelligence researchers
Miriam Cobo
David Corral Fontecha
Wilson Silva
Lara Lloret Iglesias
OODMedImAI4CE
71
0
0
28 Apr 2025
Exploring Patient Data Requirements in Training Effective AI Models for MRI-based Breast Cancer Classification
Exploring Patient Data Requirements in Training Effective AI Models for MRI-based Breast Cancer Classification
Solha Kang
W. D. Neve
Francois Rameau
Utku Ozbulak
OOD
88
0
0
22 Feb 2025
Pre-processing and Compression: Understanding Hidden Representation
  Refinement Across Imaging Domains via Intrinsic Dimension
Pre-processing and Compression: Understanding Hidden Representation Refinement Across Imaging Domains via Intrinsic Dimension
Nicholas Konz
Maciej A. Mazurowski
MedIm
57
0
0
15 Aug 2024
ConPro: Learning Severity Representation for Medical Images using
  Contrastive Learning and Preference Optimization
ConPro: Learning Severity Representation for Medical Images using Contrastive Learning and Preference Optimization
Hong Nguyen
H. Nguyen
Melinda Y. Chang
Hieu H. Pham
Shrikanth Narayanan
Michael Pazzani
59
1
0
29 Apr 2024
How to build the best medical image segmentation algorithm using foundation models: a comprehensive empirical study with Segment Anything Model
How to build the best medical image segmentation algorithm using foundation models: a comprehensive empirical study with Segment Anything Model
Han Gu
Haoyu Dong
Jichen Yang
Maciej A. Mazurowski
MedImVLM
142
21
0
15 Apr 2024
1