An AI research lab within the Department of Radiology at Duke University.
Durham, NC
Joined December 2021
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Mazurowski Lab - Duke University retweeted
I'm happy to share that our lab had three short papers and a long paper accepted at @midl_conference!
(1/4): Rethinking Perceptual Metrics for Medical Image Translation
(openreview.net/forum?id=acNu…), led by me;
Mazurowski Lab - Duke University retweeted
Excited to share our new paper, "ContourDiff: Unpaired Image Translation with Contour-Guided Diffusion Models”, which leverages domain-invariant anatomical contour representations to preserve anatomy through translation! (1/N)
arxiv.org/abs/2403.10786
Code: github.com/mazurowski-lab/Co…
🦴Introducing "SegmentAnyBone", a universal model for MRI bone segmentation. This model can segment bones in any MRI location, tackling a major challenge in medical imaging.
#SegmentAnyBone #MedicalImaging #AI #DukeSpark
🔗 Dive into our study by @Hanxue99893888, Roy Colglazier, Haoyu Dong, Jikai Zhang & more. See how we're transforming MRI analysis!: arxiv.org/abs/2401.12974
Mazurowski Lab - Duke University retweeted
How and why do neural nets learn differently from natural images vs. medical images? I’m happy to announce my new #ICLR2024 paper, “The Effect of Intrinsic Dataset Properties on Generalization”! arxiv.org/abs/2401.08865
Check out the code: github.com/mazurowski-lab/in…
More info next..
🎉 Exciting news! The paper, "Thyroid Nodules on Ultrasound in Children and Young Adults," won the Best of @AJR_Radiology award in Pediatric Imaging for 2023! It is co-authored by @JichenYang10, Laura C. Page, Lars Wagner, @benWTmd, Logan Bisset, Donald Frush, and @MazurowskiPhD.
🔍 It compared radiologists' impressions, ACR TI-RADS, & deep learning in identifying benign and malignant thyroid nodules via ultrasound. A major step in improving pediatric healthcare accuracy. Discover more: ajronline.org/doi/10.2214/AJ…… #MedicalImaging #AI
Mazurowski Lab - Duke University retweeted
This article from @benWTmd, @MazurowskiPhD, et al. compared the diagnostic performance of ACR TI-RADS and a deep learning algorithm in differentiating benign and malignant thyroid nodules on ultrasound in children and young adults.
ajronline.org/doi/10.2214/AJ…
Mazurowski Lab - Duke University retweeted
Which examples in a neural net’s training data were important for learning interpretable concepts? Our paper “Attributing Learned Concepts in Neural Networks to Training Data” has been accepted for an oral presentation at the #NeurIPS2023 ATTRIB Workshop! arxiv.org/abs/2310.03149
Mazurowski Lab - Duke University retweeted
Excited to announce our new paper in IEEE Trans. in Med. Imaging, "SWSSL: Sliding window-based self-supervised learning for anomaly detection in high-resolution images", led by Haoyu Dong!
ieeexplore.ieee.org/abstract…
Code: github.com/mazurowski-lab/sw… (1/N)
Congratulations to our Ph.D. student Haoyu Dong for his new paper in IEEE Transactions in Medical Imaging, "SWSSL: Sliding window-based self-supervised learning for anomaly detection in high-resolution images"!
ieeexplore.ieee.org/abstract…
github.com/mazurowski-lab/sw…
Mazurowski Lab - Duke University retweeted
Happy to announce that our paper, "Segment Anything Model for medical image analysis: An experimental study" has just been published in Medical Image Analysis! sciencedirect.com/science/ar…
arXiv version: arxiv.org/abs/2304.10517
code: github.com/mazurowski-lab/se…
(1/2)
Check out our Ph.D. student @Hanxue99893888 presenting the very first oral talk at #MIDL2023 last week! Her paper is "SuperMask: Generating High-resolution object masks from multi-view, unaligned low-resolution MRIs", found here: openreview.net/pdf?id=oi5psB…
Hanxue's co-authors include Hongyu He, Roy Colglazier, Jordan Axelrod, Robert French, and @MazurowskiPhD.
Mazurowski Lab - Duke University retweeted
The foreground-background imbalance problem can cause significant drops in object detection performance. Our new paper, “A systematic study of the foreground-background imbalance problem in deep learning for object detection”, is now on the arXiv! arxiv.org/abs/2306.16539 (1/n)
Mazurowski Lab - Duke University retweeted
New preprint from my lab: "Convolutional Neural Networks Rarely Learn Shape for Semantic Segmentation" by Yixin Zhang and @MazurowskiPhD! They study how segmentation CNNs learn and rely on object shape, the causes and applications of shape learning, etc. arxiv.org/abs/2305.06568
Check out our lab's new paper, “Segment Anything Model for Medical Image Analysis: an Experimental Study”! We broadly evaluated the new SAM on 11 medical image datasets for 19 segmentation tasks! arxiv.org/abs/2304.10517 (1/4)
We are already working on updating our paper, with additional datasets, other explorations of using SAM for medical image analysis, and more. Follow us to stay in the loop!
Further info: xitter.gq/MazurowskiPhD/status/1…
Code: github.com/mazurowski-lab/se…
Authors: @MazurowskiPhD, @HaoyuDong11203, @Hanxue99893888, Jichen Yang, @nick_konz and Yixin Zhang.
@duke_spark
@DukeAIHealth
Enjoy!
Does Segment Anything Model (SAM) really work on medical images?
We tested it on 11 datasets and wrote a paper:
arxiv.org/abs/2304.10517
In brief: Performance varies widely across different datasets from impressive (given zero-shot setup) to poor.
Details follow. 🧵1/14.
