Author ORCID Identifier
0000-0003-0353-9768
Document Type
Conference Paper
Disciplines
Statistics
Abstract
The integration of chemical imaging with artificial intelligence presents a compelling route toward fully digital, label-free histopathology, yet it also introduces notable challenges. While deep learning models from domains like machine vision, digital pathology, and remote sensing are readily accessible, they frequently struggle to generalize effectively to chemical imaging data, as highlighted in recent research [1]. Additionally, although foundational pathological models show potential for advancing AI-based histopathological diagnostics and prognostics, our preliminary assessments suggest they may fall short in addressing the broad spectrum of classification tasks encountered in clinical settings. In this presentation, we highlight some recent published work from our research towards developing AI for diagnostics and prognostics with chemical imaging data, and benchmark these against various foundation pathological models for task-specific applications in breast cancer.
DOI
https://doi.org/10.21427/s1z3-8q96
Recommended Citation
Suresh, Rahul; Rifqi Rafsanjani, Mohd; Jirstrom, Karin; Rahman, Arman; Gallagher, William M.; and Meade, Aidan, "Optimising AI for chemical imaging: benchmarking performance against foundation models for task-specific applications in histopathology" (2025). SAML-25 Workshop on Statistical and Machine Learning. 25.
https://arrow.tudublin.ie/saml/25
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Publication Details
Statistical and Machine Learning: Methods and Applications (SAML-25) on June 5th and 6th, 2025.
doi:10.21427/s1z3-8q96