Anticausal Learning for Inverse Problems and its Application on Optoacoustic Tomography

Artificial intelligence algorithms commonly exhibit poor performance when deployed on data whose distribution deviates from the one utilized during the training phase. While this vulnerability can be addressed post-training, doing so may necessitate a computationally intensive fine-tuning process an...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Vera, Matías, González, Martín Germán, Rey Vega, Leonardo
Formato:	Artículo publishedVersion
Lenguaje:	Español
Publicado:	FIUBA 2025
Materias:	inverse problems physics-guided models causality theory optoacoustic tomography problemas inversos modelos guiados por la física teoría de causalidad tomografía optoacústica
Acceso en línea:	https://elektron.fi.uba.ar/elektron/article/view/222 https://repositoriouba.sisbi.uba.ar/gsdl/cgi-bin/library.cgi?a=d&c=elektron&d=222_oai
Aporte de:	Repositorio Digital de la Universidad de Buenos Aires (UBA) de Universidad de Buenos Aires

id	I28-R145-222_oai
record_format	dspace
spelling	I28-R145-222_oai2026-02-11 Vera, Matías González, Martín Germán Rey Vega, Leonardo 2025-12-15 Artificial intelligence algorithms commonly exhibit poor performance when deployed on data whose distribution deviates from the one utilized during the training phase. While this vulnerability can be addressed post-training, doing so may necessitate a computationally intensive fine-tuning process and/or require a significant acquisition of new data. In this context, causality theory presents an excellent paradigm for distinguishing variation-prone mechanisms from invariant ones. This distinction would permit fitting the model exclusively to the variable components, thereby reducing the complexity of the overall problem. However, this paradigm remains under-explored in relation to inverse problems, primarily because such problems are, by their very definition, anticausal. This work undertakes an analysis of the performance and inherent limitations of fundamental algorithms in inverse problems that satisfy the criteria for anticausal learning. Specifically, these algorithms are investigated within the context of image reconstruction in optoacoustic tomography. Los algoritmos de inteligencia artificial habitualmente fallan cuando la distribución de los datos se desvía de la utilizada durante el entrenamiento. Esta vulnerabilidad puede ser corregida post-entrenamiento, pero la misma puede requerir una etapa de ajuste computacionalmente pesada y/o una gran necesidad de nuevos datos. En este contexto, la teoría de causalidad suele ser un excelente paradigma para diferenciar los mecanismos propensos a variaciones de los invariantes. Esto permitiría hacer un ajuste solamente sobre el modelo variable, reduciendo la complejidad del problema. Sin embargo, este paradigma está muy poco estudiado en lo referido a problemas inversos, principalmente porque estos problemas son por definición anticausales. En este trabajo se analiza el desempeño y limitaciones de algoritmos básicos en problemas inversos que cumplan el requisito de aprender de forma anticausal. En particular, se estudian estos algoritmos en el contexto de reconstrucción de imágenes en tomografía optoacústica. application/pdf text/html https://elektron.fi.uba.ar/elektron/article/view/222 10.37537/rev.elektron.9.2.222.2025 spa FIUBA https://elektron.fi.uba.ar/elektron/article/view/222/397 https://elektron.fi.uba.ar/elektron/article/view/222/413 Derechos de autor 2025 Matías Vera Elektron Journal; Vol. 9 No. 2 (2025); 76-83 Revista Elektron; Vol. 9 Núm. 2 (2025); 76-83 Revista Elektron; v. 9 n. 2 (2025); 76-83 2525-0159 2525-0159 inverse problems physics-guided models causality theory optoacoustic tomography problemas inversos modelos guiados por la física teoría de causalidad tomografía optoacústica Anticausal Learning for Inverse Problems and its Application on Optoacoustic Tomography Aprendizaje anticausal en problemas inversos y su aplicación a tomografía optoacústica info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion https://repositoriouba.sisbi.uba.ar/gsdl/cgi-bin/library.cgi?a=d&c=elektron&d=222_oai
institution	Universidad de Buenos Aires
institution_str	I-28
repository_str	R-145
collection	Repositorio Digital de la Universidad de Buenos Aires (UBA)
language	Español
orig_language_str_mv	spa
topic	inverse problems physics-guided models causality theory optoacoustic tomography problemas inversos modelos guiados por la física teoría de causalidad tomografía optoacústica
spellingShingle	inverse problems physics-guided models causality theory optoacoustic tomography problemas inversos modelos guiados por la física teoría de causalidad tomografía optoacústica Vera, Matías González, Martín Germán Rey Vega, Leonardo Anticausal Learning for Inverse Problems and its Application on Optoacoustic Tomography
topic_facet	inverse problems physics-guided models causality theory optoacoustic tomography problemas inversos modelos guiados por la física teoría de causalidad tomografía optoacústica
description	Artificial intelligence algorithms commonly exhibit poor performance when deployed on data whose distribution deviates from the one utilized during the training phase. While this vulnerability can be addressed post-training, doing so may necessitate a computationally intensive fine-tuning process and/or require a significant acquisition of new data. In this context, causality theory presents an excellent paradigm for distinguishing variation-prone mechanisms from invariant ones. This distinction would permit fitting the model exclusively to the variable components, thereby reducing the complexity of the overall problem. However, this paradigm remains under-explored in relation to inverse problems, primarily because such problems are, by their very definition, anticausal. This work undertakes an analysis of the performance and inherent limitations of fundamental algorithms in inverse problems that satisfy the criteria for anticausal learning. Specifically, these algorithms are investigated within the context of image reconstruction in optoacoustic tomography.
format	Artículo publishedVersion
author	Vera, Matías González, Martín Germán Rey Vega, Leonardo
author_facet	Vera, Matías González, Martín Germán Rey Vega, Leonardo
author_sort	Vera, Matías
title	Anticausal Learning for Inverse Problems and its Application on Optoacoustic Tomography
title_short	Anticausal Learning for Inverse Problems and its Application on Optoacoustic Tomography
title_full	Anticausal Learning for Inverse Problems and its Application on Optoacoustic Tomography
title_fullStr	Anticausal Learning for Inverse Problems and its Application on Optoacoustic Tomography
title_full_unstemmed	Anticausal Learning for Inverse Problems and its Application on Optoacoustic Tomography
title_sort	anticausal learning for inverse problems and its application on optoacoustic tomography
publisher	FIUBA
publishDate	2025
url	https://elektron.fi.uba.ar/elektron/article/view/222 https://repositoriouba.sisbi.uba.ar/gsdl/cgi-bin/library.cgi?a=d&c=elektron&d=222_oai
work_keys_str_mv	AT veramatias anticausallearningforinverseproblemsanditsapplicationonoptoacoustictomography AT gonzalezmartingerman anticausallearningforinverseproblemsanditsapplicationonoptoacoustictomography AT reyvegaleonardo anticausallearningforinverseproblemsanditsapplicationonoptoacoustictomography AT veramatias aprendizajeanticausalenproblemasinversosysuaplicacionatomografiaoptoacustica AT gonzalezmartingerman aprendizajeanticausalenproblemasinversosysuaplicacionatomografiaoptoacustica AT reyvegaleonardo aprendizajeanticausalenproblemasinversosysuaplicacionatomografiaoptoacustica
_version_	1857042993585848320

Anticausal Learning for Inverse Problems and its Application on Optoacoustic Tomography

Ejemplares similares