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Heuristics01:21

Heuristics

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Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
People often rely on heuristics when faced with an overload of information, limited time, low importance of the decision, limited information, or when a heuristic readily comes to mind. For...
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Quantifying Heat02:46

Quantifying Heat

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Thermal Energy Microscopically, thermal energy is the kinetic energy associated with the random motion of atoms and molecules. Temperature is a quantitative measure of “hot” or “cold”, which depends on the amount of thermal energy. When the atoms and molecules in an object are moving or vibrating quickly, they have a higher average kinetic energy (KE) (or higher thermal energy), and the object is perceived as “hot”, or it is described as being at a higher temperature. When the...
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Related Experiment Video

Updated: Jan 11, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

1.3K

More than just a heatmap: elevating XAI with rigorous evaluation metrics.

Dost Muhammad1, Malika Bendechache2

  • 1CRT-AI and ADAPT Research Centers, School of Computer Science, University of Galway, Galway, Ireland.

Frontiers in Medical Technology
|November 13, 2025
PubMed
Summary

SpikeNet, a novel deep learning framework, offers accurate and efficient tumor analysis using MRI and ultrasound. It provides explainable AI (XAI) insights, improving clinical decision support for medical imaging.

Keywords:
XAI for medical imagingXAI in healthcareXAI validationevaluation metrics for XAIexplainable DL

Related Experiment Videos

Last Updated: Jan 11, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

1.3K

Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Computational Neuroscience

Background:

  • Deep learning (DL) models show promise in tumor diagnosis and treatment planning using MRI and ultrasound.
  • However, high computational demands and limited explainability of DL hinder clinical adoption.
  • Existing explainable AI (XAI) methods often produce fragmented or misaligned saliency maps.

Purpose of the Study:

  • To develop SpikeNet, a hybrid framework combining CNNs and SNNs for efficient and explainable tumor analysis.
  • To introduce XAlign, a metric for quantifying the alignment of AI-generated explanations with expert annotations.
  • To evaluate SpikeNet's performance and explainability against traditional XAI methods.

Main Methods:

  • SpikeNet integrates CNNs for spatial encoding and SNNs for event-driven processing, featuring a native saliency module for real-time explanations.
  • The XAlign metric assesses explanation fidelity by considering regional concentration, boundary adherence, and dispersion.
  • Cross-validation was performed on TCGA-LGG (MRI) and BUSI (ultrasound) datasets, aggregating slice-level predictions to patient-level decisions.

Main Results:

  • SpikeNet achieved high accuracy and F1 scores on both TCGA-LGG (97.12% accuracy, 97.43% F1) and BUSI (98.23% accuracy, 98.32% F1) datasets.
  • The framework demonstrated low per-image latency (approx. 31ms) and high throughput (approx. 32 images/sec) on a single GPU.
  • SpikeNet's explanations, evaluated by XAlign, showed superior alignment compared to Grad-CAM, LIME, and SHAP.

Conclusions:

  • SpikeNet provides accurate, low-latency, and explainable tumor analysis for MRI and ultrasound, enhancing clinical decision support.
  • The XAlign metric offers a clinically relevant assessment of explanation fidelity, enabling robust comparisons between methods.
  • SpikeNet and XAlign show significant potential for trustworthy and efficient clinical AI applications.