762 citations · 833 across the 25 of their papers we have counts for
8 papers · 1 filter
Understanding the Inner Workings of Language Models Through Representation Dissimilarity
Davis Brown, Charles Godfrey, Nicholas Konz +2
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…
Attributing Learned Concepts in Neural Networks to Training Data
Nicholas Konz, Charles Godfrey, Madelyn Shapiro +3
By now there is substantial evidence that deep learning models learn certain human-interpretable features as part of their internal representations of data. As having the right (or…
A systematic study of the foreground-background imbalance problem in deep learning for object detection
Hanxue Gu, Haoyu Dong, Nicholas Konz +1
The class imbalance problem in deep learning has been explored in several studies, but there has yet to be a systematic analysis of this phenomenon in object detection. Here, we pr…
Segment Anything Model for Medical Image Analysis: an Experimental Study
Maciej A. Mazurowski, Haoyu Dong, Hanxue Gu +3
Training segmentation models for medical images continues to be challenging due to the limited availability of data annotations. Segment Anything Model (SAM) is a foundation model…
Unsupervised anomaly localization in high-resolution breast scans using deep pluralistic image completion
Nicholas Konz, Haoyu Dong, Maciej A. Mazurowski
Automated tumor detection in Digital Breast Tomosynthesis (DBT) is a difficult task due to natural tumor rarity, breast tissue variability, and high resolution. Given the scarcity…
Reverse Engineering Breast MRIs: Predicting Acquisition Parameters Directly from Images
Nicholas Konz, Maciej A. Mazurowski
The image acquisition parameters (IAPs) used to create MRI scans are central to defining the appearance of the images. Deep learning models trained on data acquired using certain p…
Robust Chauvenet Rejection: Powerful, but Easy to Use Outlier Detection for Heavily Contaminated Data Sets
Nicholas Konz, Daniel E. Reichart
In Maples et al. (2018) we introduced Robust Chauvenet Outlier Rejection, or RCR, a novel outlier rejection technique that evolves Chauvenet's Criterion by sequentially applying di…
Deep Learning for Breast MRI Style Transfer with Limited Training Data
Shixing Cao, Nicholas Konz, James Duncan +1
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. This is…