Interpretable Uncertainty-Aware Deep Regression with Cohort Saliency Analysis for Three-Slice CT Imaging Studies

Nouman Ahmad, Johan Öfverstedt, Sambit Tarai, Göran Bergström, Håkan Ahlström, Joel Kullberg
Proceedings of The 7nd International Conference on Medical Imaging with Deep Learning, PMLR 250:17-32, 2024.

Abstract

Obesity is associated with an increased risk of morbidity and mortality. Achieving a healthy body composition, which involves maintaining a balance between fat and muscle mass, is important for metabolic health and preventing chronic diseases. Computed tomography (CT) imaging offers detailed insights into the body’s internal structure, aiding in understanding body composition and its related factors. In this feasibility study, we utilized CT image data from 2,724 subjects from the large metabolic health cohort studies SCAPIS and IGT. We train and evaluate an uncertainty-aware deep regression based ResNet-50 network, which outputs its prediction as mean and variance, for quantification of cross-sectional areas of liver, visceral adipose tissue (VAT), and thigh muscle. This was done using collages of three single-slice CT images from the liver, abdomen, and thigh regions. The model demonstrated promising results with the evaluation metrics – including R-squared ($R^2$) and mean absolute error (MAE) for predictions. Additionally, for interpretability, the model was evaluated with saliency analysis based on Grad-CAM (Gradient-weighted Class Activation Mapping) at stages 2, 3, and 4 of the network. Deformable image registration to a template subject further enabled cohort saliency analysis that provide group-wise visualization of image regions of importance for associations to biomarkers of interest. We found that the networks focus on relevant regions for each target, according to prior knowledge. The source code is available at: \url{https://github.com/noumannahmad/dr_3slice_ct}.

Cite this Paper

BibTeX
@InProceedings{pmlr-v250-ahmad24a, title = {Interpretable Uncertainty-Aware Deep Regression with Cohort Saliency Analysis for Three-Slice CT Imaging Studies}, author = {Ahmad, Nouman and \"Ofverstedt, Johan and Tarai, Sambit and Bergstr\"om, G\"oran and Ahlstr\"om, H\r{a}kan and Kullberg, Joel}, booktitle = {Proceedings of The 7nd International Conference on Medical Imaging with Deep Learning}, pages = {17--32}, year = {2024}, editor = {Burgos, Ninon and Petitjean, Caroline and Vakalopoulou, Maria and Christodoulidis, Stergios and Coupe, Pierrick and Delingette, Hervé and Lartizien, Carole and Mateus, Diana}, volume = {250}, series = {Proceedings of Machine Learning Research}, month = {03--05 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v250/main/assets/ahmad24a/ahmad24a.pdf}, url = {https://proceedings.mlr.press/v250/ahmad24a.html}, abstract = {Obesity is associated with an increased risk of morbidity and mortality. Achieving a healthy body composition, which involves maintaining a balance between fat and muscle mass, is important for metabolic health and preventing chronic diseases. Computed tomography (CT) imaging offers detailed insights into the body’s internal structure, aiding in understanding body composition and its related factors. In this feasibility study, we utilized CT image data from 2,724 subjects from the large metabolic health cohort studies SCAPIS and IGT. We train and evaluate an uncertainty-aware deep regression based ResNet-50 network, which outputs its prediction as mean and variance, for quantification of cross-sectional areas of liver, visceral adipose tissue (VAT), and thigh muscle. This was done using collages of three single-slice CT images from the liver, abdomen, and thigh regions. The model demonstrated promising results with the evaluation metrics – including R-squared ($R^2$) and mean absolute error (MAE) for predictions. Additionally, for interpretability, the model was evaluated with saliency analysis based on Grad-CAM (Gradient-weighted Class Activation Mapping) at stages 2, 3, and 4 of the network. Deformable image registration to a template subject further enabled cohort saliency analysis that provide group-wise visualization of image regions of importance for associations to biomarkers of interest. We found that the networks focus on relevant regions for each target, according to prior knowledge. The source code is available at: \url{https://github.com/noumannahmad/dr_3slice_ct}.} }
Endnote
%0 Conference Paper %T Interpretable Uncertainty-Aware Deep Regression with Cohort Saliency Analysis for Three-Slice CT Imaging Studies %A Nouman Ahmad %A Johan Öfverstedt %A Sambit Tarai %A Göran Bergström %A Håkan Ahlström %A Joel Kullberg %B Proceedings of The 7nd International Conference on Medical Imaging with Deep Learning %C Proceedings of Machine Learning Research %D 2024 %E Ninon Burgos %E Caroline Petitjean %E Maria Vakalopoulou %E Stergios Christodoulidis %E Pierrick Coupe %E Hervé Delingette %E Carole Lartizien %E Diana Mateus %F pmlr-v250-ahmad24a %I PMLR %P 17--32 %U https://proceedings.mlr.press/v250/ahmad24a.html %V 250 %X Obesity is associated with an increased risk of morbidity and mortality. Achieving a healthy body composition, which involves maintaining a balance between fat and muscle mass, is important for metabolic health and preventing chronic diseases. Computed tomography (CT) imaging offers detailed insights into the body’s internal structure, aiding in understanding body composition and its related factors. In this feasibility study, we utilized CT image data from 2,724 subjects from the large metabolic health cohort studies SCAPIS and IGT. We train and evaluate an uncertainty-aware deep regression based ResNet-50 network, which outputs its prediction as mean and variance, for quantification of cross-sectional areas of liver, visceral adipose tissue (VAT), and thigh muscle. This was done using collages of three single-slice CT images from the liver, abdomen, and thigh regions. The model demonstrated promising results with the evaluation metrics – including R-squared ($R^2$) and mean absolute error (MAE) for predictions. Additionally, for interpretability, the model was evaluated with saliency analysis based on Grad-CAM (Gradient-weighted Class Activation Mapping) at stages 2, 3, and 4 of the network. Deformable image registration to a template subject further enabled cohort saliency analysis that provide group-wise visualization of image regions of importance for associations to biomarkers of interest. We found that the networks focus on relevant regions for each target, according to prior knowledge. The source code is available at: \url{https://github.com/noumannahmad/dr_3slice_ct}.
APA
Ahmad, N., Öfverstedt, J., Tarai, S., Bergström, G., Ahlström, H. & Kullberg, J.. (2024). Interpretable Uncertainty-Aware Deep Regression with Cohort Saliency Analysis for Three-Slice CT Imaging Studies. Proceedings of The 7nd International Conference on Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research 250:17-32 Available from https://proceedings.mlr.press/v250/ahmad24a.html.

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