25 research outputs found
Learning Physics-Inspired Regularization for Medical Image Registration with Hypernetworks
Medical image registration aims at identifying the spatial deformation
between images of the same anatomical region and is fundamental to image-based
diagnostics and therapy. To date, the majority of the deep learning-based
registration methods employ regularizers that enforce global spatial
smoothness, e.g., the diffusion regularizer. However, such regularizers are not
tailored to the data and might not be capable of reflecting the complex
underlying deformation. In contrast, physics-inspired regularizers promote
physically plausible deformations. One such regularizer is the linear elastic
regularizer which models the deformation of elastic material. These
regularizers are driven by parameters that define the material's physical
properties. For biological tissue, a wide range of estimations of such
parameters can be found in the literature and it remains an open challenge to
identify suitable parameter values for successful registration. To overcome
this problem and to incorporate physical properties into learning-based
registration, we propose to use a hypernetwork that learns the effect of the
physical parameters of a physics-inspired regularizer on the resulting spatial
deformation field. In particular, we adapt the HyperMorph framework to learn
the effect of the two elasticity parameters of the linear elastic regularizer.
Our approach enables the efficient discovery of suitable, data-specific
physical parameters at test time.Comment: Manuscript accepted at SPIE Medical Imaging 202
Dynamic inter-treatment information sharing for heterogeneous treatment effects estimation
Existing heterogeneous treatment effects learners, also known as conditional average treatment effects (CATE) learners, lack a general mechanism for end-to-end inter-treatment information sharing, and data have to be split among potential outcome functions to train CATE learners which can lead to biased estimates with limited observational datasets. To address this issue, we propose a novel deep learning-based framework to train CATE learners that facilitates dynamic end-to-end information sharing among treatment groups. The framework is based on \textit{soft weight sharing} of \textit{hypernetworks}, which offers advantages such as parameter efficiency, faster training, and improved results. The proposed framework complements existing CATE learners and introduces a new class of uncertainty-aware CATE learners that we refer to as \textit{HyperCATE}. We develop HyperCATE versions of commonly used CATE learners and evaluate them on IHDP, ACIC-2016, and Twins benchmarks. Our experimental results show that the proposed framework improves the CATE estimation error via counterfactual inference, with increasing effectiveness for smaller datasets