Generating synthetic data for medical imaging
Artificial intelligence (AI) models for medical imaging tasks, such as classification or
segmentation, require large and diverse datasets of images. However, due to privacy and …
segmentation, require large and diverse datasets of images. However, due to privacy and …
TorchXRayVision: A library of chest X-ray datasets and models
TorchXRayVision is an open source software library for working with chest X-ray datasets
and deep learning models. It provides a common interface and common pre-processing …
and deep learning models. It provides a common interface and common pre-processing …
Dreamr: Diffusion-driven counterfactual explanation for functional mri
Deep learning analyses have offered sensitivity leaps in detection of cognition-related
variables from functional MRI (fMRI) measurements of brain responses. Yet, as deep models …
variables from functional MRI (fMRI) measurements of brain responses. Yet, as deep models …
Problems in the deployment of machine-learned models in health care
• Decision-support systems or clinical prediction tools based on machine learning (including
the special case of deep learning) are similar to clinical support tools developed using …
the special case of deep learning) are similar to clinical support tools developed using …
Explaining the black-box smoothly—a counterfactual approach
Abstract We propose a BlackBox Counterfactual Explainer, designed to explain image
classification models for medical applications. Classical approaches (eg,, saliency maps) …
classification models for medical applications. Classical approaches (eg,, saliency maps) …
Merlin: A vision language foundation model for 3d computed tomography
L Blankemeier, JP Cohen, A Kumar… - Research …, 2024 - pmc.ncbi.nlm.nih.gov
Over 85 million computed tomography (CT) scans are performed annually in the US, of
which approximately one quarter focus on the abdomen. Given the current shortage of both …
which approximately one quarter focus on the abdomen. Given the current shortage of both …
Training calibration-based counterfactual explainers for deep learning models in medical image analysis
The rapid adoption of artificial intelligence methods in healthcare is coupled with the critical
need for techniques to rigorously introspect models and thereby ensure that they behave …
need for techniques to rigorously introspect models and thereby ensure that they behave …
Diffusion models for counterfactual generation and anomaly detection in brain images
A Fontanella, G Mair, J Wardlaw… - … on Medical Imaging, 2024 - ieeexplore.ieee.org
Segmentation masks of pathological areas are useful in many medical applications, such as
brain tumour and stroke management. Moreover, healthy counterfactuals of diseased …
brain tumour and stroke management. Moreover, healthy counterfactuals of diseased …
Fast diffusion-based counterfactuals for shortcut removal and generation
Shortcut learning is when a model–eg a cardiac disease classifier–exploits correlations
between the target label and a spurious shortcut feature, eg a pacemaker, to predict the …
between the target label and a spurious shortcut feature, eg a pacemaker, to predict the …
Right for the Wrong Reason: Can Interpretable ML Techniques Detect Spurious Correlations?
While deep neural network models offer unmatched classification performance, they are
prone to learning spurious correlations in the data. Such dependencies on confounding …
prone to learning spurious correlations in the data. Such dependencies on confounding …