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Found 10 papers, 9 papers with code
Robust Learning via Conditional Prevalence Adjustment
CoPA assumes that (1) generation mechanism is stable, i. e. label Y and confounding variable(s) Z generate X, and (2) the unstable conditional prevalence in each site E fully accounts for the unstable correlations between X and Y .
A Framework for Interpretability in Machine Learning for Medical Imaging
To answer these questions, we identify a need to formalize the goals and elements of interpretability in MLMI.
A Robust and Interpretable Deep Learning Framework for Multi-modal Registration via Keypoints
Our core insight which addresses these shortcomings is that corresponding keypoints between images can be used to obtain the optimal transformation via a differentiable closed-form expression.
A Flexible Nadaraya-Watson Head Can Offer Explainable and Calibrated Classification
The first is the label weights, and the second is our novel concept of the ``support influence function,'' which is an easy-to-compute metric that quantifies the influence of a support element on the prediction for a given query.
Computing Multiple Image Reconstructions with a Single Hypernetwork
The typical approach is to train the model for a hyperparameter setting determined with some empirical or theoretical justification.
Hyper-Convolutions via Implicit Kernels for Medical Imaging
The key building block of a CNN is the convolutional kernel that aggregates information from the pixel neighborhood and shares weights across all pixels.
Joint Optimization of Hadamard Sensing and Reconstruction in Compressed Sensing Fluorescence Microscopy
Compressed sensing fluorescence microscopy (CS-FM) proposes a scheme whereby less measurements are collected during sensing and reconstruction is performed to recover the image.
Regularization-Agnostic Compressed Sensing MRI Reconstruction with Hypernetworks
In this paper, we explore a novel strategy of using a hypernetwork to generate the parameters of a separate reconstruction network as a function of the regularization weight(s), resulting in a regularization-agnostic reconstruction model.
Neural Network-based Reconstruction in Compressed Sensing MRI Without Fully-sampled Training Data
In this paper, we explore a novel strategy to train an unrolled reconstruction network in an unsupervised fashion by adopting a loss function widely-used in classical optimization schemes.
Deep-learning-based Optimization of the Under-sampling Pattern in MRI
In our experiments, we demonstrate that LOUPE-optimized under-sampling masks are data-dependent, varying significantly with the imaged anatomy, and perform well with different reconstruction methods.
