no code implementations • 10 Apr 2024 • Nicholas Konz, YuWen Chen, Hanxue Gu, Haoyu Dong, Maciej A. Mazurowski
Modern medical image translation methods use generative models for tasks such as the conversion of CT images to MRI.
no code implementations • 16 Mar 2024 • YuWen Chen, Nicholas Konz, Hanxue Gu, Haoyu Dong, Yaqian Chen, Lin Li, Jisoo Lee, Maciej A. Mazurowski
We evaluate our method by training a segmentation model on images translated from CT to MRI with their original CT masks and testing its performance on real MRIs.
1 code implementation • 14 Feb 2024 • Haoyu Dong, Nicholas Konz, Hanxue Gu, Maciej A. Mazurowski
Here, we approach such a task, of adapting a medical image segmentation model with only a single unlabeled test image.
1 code implementation • 7 Feb 2024 • Nicholas Konz, YuWen Chen, Haoyu Dong, Maciej A. Mazurowski
Diffusion models have enabled remarkably high-quality medical image generation, yet it is challenging to enforce anatomical constraints in generated images.
1 code implementation • 16 Jan 2024 • Nicholas Konz, Maciej A. Mazurowski
We address this gap in knowledge by establishing and empirically validating a generalization scaling law with respect to $d_{data}$, and propose that the substantial scaling discrepancy between the two considered domains may be at least partially attributed to the higher intrinsic ``label sharpness'' ($K_\mathcal{F}$) of medical imaging datasets, a metric which we propose.
no code implementations • 23 Oct 2023 • Davis Brown, Charles Godfrey, Nicholas Konz, Jonathan Tu, Henry Kvinge
As language models are applied to an increasing number of real-world applications, understanding their inner workings has become an important issue in model trust, interpretability, and transparency.
no code implementations • 4 Oct 2023 • Nicholas Konz, Charles Godfrey, Madelyn Shapiro, Jonathan Tu, Henry Kvinge, Davis Brown
By now there is substantial evidence that deep learning models learn certain human-interpretable features as part of their internal representations of data.
no code implementations • 28 Jun 2023 • Hanxue Gu, Haoyu Dong, Nicholas Konz, Maciej A. Mazurowski
We experimentally study the effects of different aspects of F-B imbalance (object size, number of objects, dataset size, object type) on detection performance.
1 code implementation • 4 May 2023 • Nicholas Konz, Haoyu Dong, Maciej A. Mazurowski
Given the scarcity of abnormal images and the abundance of normal images for this problem, an anomaly detection/localization approach could be well-suited.
2 code implementations • 20 Apr 2023 • Maciej A. Mazurowski, Haoyu Dong, Hanxue Gu, Jichen Yang, Nicholas Konz, Yixin Zhang
We conclude that SAM shows impressive zero-shot segmentation performance for certain medical imaging datasets, but moderate to poor performance for others.
1 code implementation • 8 Mar 2023 • Nicholas Konz, Maciej A. Mazurowski
The image acquisition parameters (IAPs) used to create MRI scans are central to defining the appearance of the images.
1 code implementation • 5 Jan 2023 • Shixing Cao, Nicholas Konz, James Duncan, Maciej A. Mazurowski
In this work we introduce a novel medical image style transfer method, StyleMapper, that can transfer medical scans to an unseen style with access to limited training data.
1 code implementation • 6 Jul 2022 • Nicholas Konz, Hanxue Gu, Haoyu Dong, Maciej A. Mazurowski
These results give a more principled underpinning for the intuition that radiological images can be more challenging to apply deep learning to than natural image datasets common to machine learning research.
no code implementations • 22 Nov 2021 • Yifan Zhang, Haoyu Dong, Nicholas Konz, Hanxue Gu, Maciej A. Mazurowski
Specifically, we propose a novel modification of visual transformer (ViT) on image feature patches to connect the feature patches of a tumor with healthy backgrounds of breast images and form a more robust backbone for tumor detection.