Search Results for author:
Found 14 papers, 8 papers with code
Rethinking Perceptual Metrics for Medical Image Translation
Modern medical image translation methods use generative models for tasks such as the conversion of CT images to MRI.
ContourDiff: Unpaired Image Translation with Contour-Guided Diffusion Models
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.
Medical Image Segmentation with InTEnt: Integrated Entropy Weighting for Single Image Test-Time Adaptation
Here, we approach such a task, of adapting a medical image segmentation model with only a single unlabeled test image.
Anatomically-Controllable Medical Image Generation with Segmentation-Guided Diffusion Models
Diffusion models have enabled remarkably high-quality medical image generation, yet it is challenging to enforce anatomical constraints in generated images.
The Effect of Intrinsic Dataset Properties on Generalization: Unraveling Learning Differences Between Natural and Medical Images
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.
Understanding the Inner Workings of Language Models Through Representation Dissimilarity
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.
Attributing Learned Concepts in Neural Networks to Training Data
By now there is substantial evidence that deep learning models learn certain human-interpretable features as part of their internal representations of data.
A systematic study of the foreground-background imbalance problem in deep learning for object detection
We experimentally study the effects of different aspects of F-B imbalance (object size, number of objects, dataset size, object type) on detection performance.
Unsupervised anomaly localization in high-resolution breast scans using deep pluralistic image completion
Given the scarcity of abnormal images and the abundance of normal images for this problem, an anomaly detection/localization approach could be well-suited.
Segment Anything Model for Medical Image Analysis: an Experimental Study
We conclude that SAM shows impressive zero-shot segmentation performance for certain medical imaging datasets, but moderate to poor performance for others.
Reverse Engineering Breast MRIs: Predicting Acquisition Parameters Directly from Images
The image acquisition parameters (IAPs) used to create MRI scans are central to defining the appearance of the images.
Deep Learning for Breast MRI Style Transfer with Limited Training Data
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.
The Intrinsic Manifolds of Radiological Images and their Role in Deep Learning
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.
Lightweight Transformer Backbone for Medical Object Detection
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.
