Nicholas Konz

Orcid: 0000-0003-0230-1598

According to our database¹, Nicholas Konz authored at least 31 papers between 2021 and 2026.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of four.

Timeline

Legend:

Book In proceedings Article PhD thesis Dataset Other

Bibliography

2026

ConceptM<sup>3</sup>oE: Concept-Guided Multimodal Mixture of Experts for Interpretable Computational Pathology.

[BibT_eX]

[DOI]

Abdurrahmaan Baghdadi

CoRR, May, 2026

Accelerating Volumetric Medical Image Annotation via Short-Long Memory SAM 2.

[BibT_eX]

[DOI]

IEEE Trans. Medical Imaging, April, 2026

Clinically-Informed Modeling for Pediatric Brain Tumor Classification from Whole-Slide Histopathology Images.

[BibT_eX]

[DOI]

CoRR, April, 2026

PathMoE: Interpretable Multimodal Interaction Experts for Pediatric Brain Tumor Classification.

[BibT_eX]

[DOI]

CoRR, March, 2026

Fréchet radiomic distance (FRD): A versatile metric for comparing medical imaging datasets.

[BibT_eX]

[DOI]

Medical Image Anal., 2026

Quantifying the Limits of Segmentation Foundation Models: Modeling Challenges in Segmenting Tree-Like and Low-Contrast Objects.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2026

2025

SegmentAnyMuscle: A universal muscle segmentation model across different locations in MRI.

[BibT_eX]

[DOI]

CoRR, June, 2025

MRI-CORE: A Foundation Model for Magnetic Resonance Imaging.

[BibT_eX]

[DOI]

CoRR, June, 2025

GuidedMorph: Two-Stage Deformable Registration for Breast MRI.

[BibT_eX]

[DOI]

CoRR, May, 2025

Are Vision Foundation Models Ready for Out-of-the-Box Medical Image Registration?

[BibT_eX]

[DOI]

Proceedings of the Artificial Intelligence and Imaging for Diagnostic and Treatment Challenges in Breast Care, 2025

2024

Quantifying the Limits of Segment Anything Model: Analyzing Challenges in Segmenting Tree-Like and Low-Contrast Structures.

[BibT_eX]

[DOI]

CoRR, 2024

RaD: A Metric for Medical Image Distribution Comparison in Out-of-Domain Detection and Other Applications.

[BibT_eX]

[DOI]

CoRR, 2024

The Impact of Scanner Domain Shift on Deep Learning Performance in Medical Imaging: an Experimental Study.

[BibT_eX]

[DOI]

CoRR, 2024

Pre-processing and Compression: Understanding Hidden Representation Refinement Across Imaging Domains via Intrinsic Dimension.

[BibT_eX]

[DOI]

Nicholas Konz

Maciej A. Mazurowski

CoRR, 2024

Rethinking Perceptual Metrics for Medical Image Translation.

[BibT_eX]

[DOI]

CoRR, 2024

ContourDiff: Unpaired Image Translation with Contour-Guided Diffusion Models.

[BibT_eX]

[DOI]

CoRR, 2024

Anatomically-Controllable Medical Image Generation with Segmentation-Guided Diffusion Models.

[BibT_eX]

[DOI]

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2024, 2024

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

[BibT_eX]

[DOI]

Nicholas Konz

Maciej A. Mazurowski

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Medical Image Segmentation with InTEnt: Integrated Entropy Weighting for Single Image Test-Time Adaptation.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

2023

SWSSL: Sliding Window-Based Self-Supervised Learning for Anomaly Detection in High-Resolution Images.

[BibT_eX]

[DOI]

IEEE Trans. Medical Imaging, December, 2023

Segment anything model for medical image analysis: An experimental study.

[BibT_eX]

[DOI]

Medical Image Anal., October, 2023

Deep Learning for Breast MRI Style Transfer with Limited Training Data.

[BibT_eX]

[DOI]

J. Digit. Imaging, April, 2023

Computer Vision Techniques in Manufacturing.

[BibT_eX]

[DOI]

Longfei Zhou

Lin Zhang

Nicholas Konz

IEEE Trans. Syst. Man Cybern. Syst., 2023

Unsupervised anomaly localization in high-resolution breast scans using deep pluralistic image completion.

[BibT_eX]

[DOI]

Nicholas Konz

Haoyu Dong

Maciej A. Mazurowski

Medical Image Anal., 2023

Attributing Learned Concepts in Neural Networks to Training Data.

[BibT_eX]

[DOI]

CoRR, 2023

A systematic study of the foreground-background imbalance problem in deep learning for object detection.

[BibT_eX]

[DOI]

CoRR, 2023

Reverse Engineering Breast MRIs: Predicting Acquisition Parameters Directly from Images.

[BibT_eX]

[DOI]

Nicholas Konz

Maciej A. Mazurowski

Proceedings of the Medical Imaging with Deep Learning, 2023

Understanding the Inner-workings of Language Models Through Representation Dissimilarity.

[BibT_eX]

[DOI]

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

2022

Lightweight Transformer Backbone for Medical Object Detection.

[BibT_eX]

[DOI]

Proceedings of the Cancer Prevention Through Early Detection, 2022

The Intrinsic Manifolds of Radiological Images and Their Role in Deep Learning.

[BibT_eX]

[DOI]

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2022, 2022

2021

REPLICA: Enhanced Feature Pyramid Network by Local Image Translation and Conjunct Attention for High-Resolution Breast Tumor Detection.

[BibT_eX]

[DOI]

CoRR, 2021

Nicholas Konz

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Bibliography

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