SpecReX: Explainable AI for Raman Spectroscopy

• URL: http://arxiv.org/abs/2503.14567v1
• Date: Tue, 18 Mar 2025 10:49:15 GMT
• Title: SpecReX: Explainable AI for Raman Spectroscopy
• Abstract summary: We introduce SpecReX, specifically adapted to explaining Raman spectra.<n>SpecReX uses the theory of actual causality to rank causal responsibility in a spectrum.<n>We establish that SpecReX localizes to the known differences between classes.
• Score: 4.541069830146568
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Raman spectroscopy is becoming more common for medical diagnostics with deep learning models being increasingly used to leverage its full potential. However, the opaque nature of such models and the sensitivity of medical diagnosis together with regulatory requirements necessitate the need for explainable AI tools. We introduce SpecReX, specifically adapted to explaining Raman spectra. SpecReX uses the theory of actual causality to rank causal responsibility in a spectrum, quantified by iteratively refining mutated versions of the spectrum and testing if it retains the original classification. The explanations provided by SpecReX take the form of a responsibility map, highlighting spectral regions most responsible for the model to make a correct classification. To assess the validity of SpecReX, we create increasingly complex simulated spectra, in which a "ground truth" signal is seeded, to train a classifier. We then obtain SpecReX explanations and compare the results with another explainability tool. By using simulated spectra we establish that SpecReX localizes to the known differences between classes, under a number of conditions. This provides a foundation on which we can find the spectral features which differentiate disease classes. This is an important first step in proving the validity of SpecReX.

Related papers

• AstroSpecLM: A Spectrum-Language Model for Evidence-Grounded Astronomical Spectral Analysis [2.6513685737789463]
  This paper presents AstroSpecLM, a spectrum-language model that connects one-dimensional DESI spectra with Qwen3-4B to answer questions.<n>We first distill each spectrum into a compact set of catalog- and spectrum-derived facts, then use these facts as references to generate instruction-following conversations.<n>Our results indicate that grounding a language model in one-dimensional scientific spectra is feasible, and that fact-mediated instruction data yields a model capable of both prediction and explanation.
  arXiv  Detail & Related papers  (2026-09-07T06:44:01Z)
• SpecCal: Ambiguity-Aware Candidate Calibration for Infrared Spectrum-Based Molecular Structure Reconstruction [7.290523865260595]
  Inferring molecular structures from infrared (IR) spectra is a fundamental yet challenging problem.<n>We propose SpecCal, a training-free candidate calibration framework for IR-to-molecule prediction.
  arXiv  Detail & Related papers  (2026-07-30T07:21:55Z)
• Spectral Model eXplainer: a chemically-grounded explainability framework for spectral-based machine learning models [0.6487259764989486]
  This study introduces the Spectral Model eXplainer (SMX), a post-hoc, global, model-agnostic XAI framework that explains spectral classifiers through expert-informed spectral zones.<n>SMX summarizes each zone via PCA, defines quantile-based logical predicates, estimates relevance with perturbations, and aggregates bag-wise rankings in a directed weighted graph.
  arXiv  Detail & Related papers  (2026-05-04T15:01:04Z)
• Extracting latent representations from X-ray spectra. Classification, regression, and accretion signatures of Chandra sources [0.0]
  This work aims to develop a compact and physically meaningful representation of Chandra X-ray spectra using deep learning.<n>We use a transformer-based autoencoder to compress X-ray spectra.<n>We evaluate the learned representation in terms of spectral reconstruction accuracy, clustering performance, and correlation with physical quantities.
  arXiv  Detail & Related papers  (2025-10-15T21:20:32Z)
• From Eigenmodes to Proofs: Integrating Graph Spectral Operators with Symbolic Interpretable Reasoning [0.0]
  We introduce Spectral NSR, a fully spectral neuro-symbolic reasoning framework.<n>It embeds logical rules as spectral templates and performs inference directly in the graph spectral domain.<n>We show that Spectral NSR achieves superior accuracy, faster inference, improved robustness to adversarial perturbations, and higher interpretability compared to leading baselines.
  arXiv  Detail & Related papers  (2025-09-07T01:12:20Z)
• Generalize across Homophily and Heterophily: Hybrid Spectral Graph Pre-Training and Prompt Tuning [32.2698194084332]
  Existing methods fail to handle diverse spectral distributions in real-world graphs with varying homophily.<n>We propose a novel framework that ensures spectral alignment throughout both pre-training and prompt-tuning.<n>Our experiments validate the effectiveness under both transductive and inductive learning settings.
  arXiv  Detail & Related papers  (2025-08-15T08:55:57Z)
• SpectrumFM: A New Paradigm for Spectrum Cognition [65.65474629224558]
  We propose a spectrum foundation model, termed SpectrumFM, which provides a new paradigm for spectrum cognition.<n>An innovative spectrum encoder that exploits the convolutional neural networks is proposed to effectively capture both fine-grained local signal structures and high-level global dependencies in the spectrum data.<n>Two novel self-supervised learning tasks, namely masked reconstruction and next-slot signal prediction, are developed for pre-training SpectrumFM, enabling the model to learn rich and transferable representations.
  arXiv  Detail & Related papers  (2025-08-02T14:40:50Z)
• DiffSpectra: Molecular Structure Elucidation from Spectra using Diffusion Models [66.41802970528133]
  Molecular structure elucidation from spectra is a foundational problem in chemistry.<n>Traditional methods rely heavily on expert interpretation and lack scalability.<n>We present DiffSpectra, a generative framework that directly infers both 2D and 3D molecular structures from multi-modal spectral data.
  arXiv  Detail & Related papers  (2025-07-09T13:57:20Z)
• Spectra-to-Structure and Structure-to-Spectra Inference Across the Periodic Table [60.78615287040791]
  XAStruct is a learning framework capable of both predicting XAS spectra from crystal structures and inferring local structural descriptors from XAS input.<n>XAStruct is trained on a large-scale dataset spanning over 70 elements across the periodic table.
  arXiv  Detail & Related papers  (2025-06-13T15:58:05Z)
• CARL: Camera-Agnostic Representation Learning for Spectral Image Analysis [75.25966323298003]
  Spectral imaging offers promising applications across diverse domains, including medicine and urban scene understanding. variability in channel dimensionality and captured wavelengths among spectral cameras impede the development of AI-driven methodologies. We introduce $textbfCARL$, a model for $textbfC$amera-$textbfA$gnostic $textbfR$esupervised $textbfL$ across RGB, multispectral, and hyperspectral imaging modalities.
  arXiv  Detail & Related papers  (2025-04-27T13:06:40Z)
• Point-Calibrated Spectral Neural Operators [54.13671100638092]
  We introduce Point-Calibrated Spectral Transform, which learns operator mappings by approximating functions with the point-level adaptive spectral basis. Point-Calibrated Spectral Neural Operators learn operator mappings by approximating functions with the point-level adaptive spectral basis.
  arXiv  Detail & Related papers  (2024-10-15T08:19:39Z)
• Universal Spectral Transfer with Physical Prior-Informed Deep Generative Learning [9.603403541272746]
  We introduce SpectroGen, a novel physical prior-informed deep generative model for generating relevant spectral signatures. Results show generating with 99% correlation and 0.01 root mean square error with superior resolution than experimentally acquired ground truth spectra.
  arXiv  Detail & Related papers  (2024-07-22T23:31:10Z)
• SpectralNeRF: Physically Based Spectral Rendering with Neural Radiance Field [70.15900280156262]
  We propose an end-to-end Neural Radiance Field (NeRF)-based architecture for high-quality physically based rendering from a novel spectral perspective. SpectralNeRF is superior to recent NeRF-based methods when synthesizing new views on synthetic and real datasets.
  arXiv  Detail & Related papers  (2023-12-14T07:19:31Z)
• HoloNets: Spectral Convolutions do extend to Directed Graphs [59.851175771106625]
  Conventional wisdom dictates that spectral convolutional networks may only be deployed on undirected graphs. Here we show this traditional reliance on the graph Fourier transform to be superfluous. We provide a frequency-response interpretation of newly developed filters, investigate the influence of the basis used to express filters and discuss the interplay with characteristic operators on which networks are based.
  arXiv  Detail & Related papers  (2023-10-03T17:42:09Z)
• Specformer: Spectral Graph Neural Networks Meet Transformers [51.644312964537356]
  Spectral graph neural networks (GNNs) learn graph representations via spectral-domain graph convolutions. We introduce Specformer, which effectively encodes the set of all eigenvalues and performs self-attention in the spectral domain. By stacking multiple Specformer layers, one can build a powerful spectral GNN.
  arXiv  Detail & Related papers  (2023-03-02T07:36:23Z)
• RamanNet: A generalized neural network architecture for Raman Spectrum Analysis [4.670045009583903]
  Raman spectroscopy provides a vibrational profile of the molecules and can be used to identify different kind of materials. Despite the recent rise in Raman spectra data volume, there has not been any significant effort in developing generalized machine learning methods for Raman spectra analysis. We examine, experiment and evaluate existing methods and conjecture that neither current sequential models nor traditional machine learning models are satisfactorily sufficient to analyze Raman spectra.
  arXiv  Detail & Related papers  (2022-01-20T23:15:25Z)
• Unsupervised Spectral Unmixing For Telluric Correction Using A Neural Network Autoencoder [58.720142291102135]
  We present a neural network autoencoder approach for extracting a telluric transmission spectrum from a large set of high-precision observed solar spectra from the HARPS-N radial velocity spectrograph.
  arXiv  Detail & Related papers  (2021-11-17T12:54:48Z)
• MassFormer: Tandem Mass Spectrum Prediction for Small Molecules using Graph Transformers [3.2951121243459522]
  Tandem mass spectra capture fragmentation patterns that provide key structural information about a molecule. For over seventy years, spectrum prediction has remained a key challenge in the field. We propose a new model, MassFormer, for accurately predicting tandem mass spectra.
  arXiv  Detail & Related papers  (2021-11-08T20:55:15Z)
• A Novel CropdocNet for Automated Potato Late Blight Disease Detection from the Unmanned Aerial Vehicle-based Hyperspectral Imagery [3.3283767441645478]
  Late blight disease is one of the most destructive diseases in potato crop, leading to serious yield losses globally. Current farm practices in crop disease diagnosis are based on manual visual inspection, which is costly, time consuming, subject to individual bias. Recent advances in imaging sensors (e.g. RGB, multiple spectral and hyperspectral cameras), remote sensing and machine learning offer the opportunity to address this challenge.
  arXiv  Detail & Related papers  (2021-07-28T11:18:48Z)
• Disentangled Representation Learning for Astronomical Chemical Tagging [0.0]
  We present a method for isolating the chemical factors of variation in stellar spectra from those of other parameters. This enables us to build a spectral projection for each star with these parameters removed. Our work demonstrates the feasibility of data-driven abundance-free chemical tagging.
  arXiv  Detail & Related papers  (2021-03-10T22:55:44Z)
• SpecTr: Spectral Transformer for Hyperspectral Pathology Image Segmentation [14.34998033157658]
  We name our method Spectral Transformer (SpecTr), which has a strong ability to model long-range dependency among spectral bands. SpecTr outperforms other competing methods in a hyperspectral pathology image segmentation benchmark without the need of pre-training.
  arXiv  Detail & Related papers  (2021-03-05T11:12:22Z)
• Persistent spectral based machine learning (PerSpect ML) for drug design [0.0]
  We propose persistent spectral based machine learning (PerSpect ML) models for drug design. We consider 11 persistent spectral variables and use them as the feature for machine learning models in protein-ligand binding affinity prediction. Our results, for all these databases, are better than all existing models, as far as we know.
  arXiv  Detail & Related papers  (2020-02-03T07:14:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.