Explainability Through Human-Centric Design for XAI in Lung Cancer Detection

• URL: http://arxiv.org/abs/2505.09755v2
• Date: Fri, 23 May 2025 10:43:45 GMT
• Title: Explainability Through Human-Centric Design for XAI in Lung Cancer Detection
• Authors: Amy Rafferty, Rishi Ramaesh, Ajitha Rajan,
• Abstract summary: We present XpertXAI, a generalizable expert-driven model for interpretable lung cancer diagnosis.<n>XpertXAI preserves human-interpretable clinical concepts while scaling to detect multiple lung pathologies.<n>We find that existing techniques frequently fail to produce clinically meaningful explanations.
• Score: 2.380494879018844
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning models have shown promise in lung pathology detection from chest X-rays, but widespread clinical adoption remains limited due to opaque model decision-making. In prior work, we introduced ClinicXAI, a human-centric, expert-guided concept bottleneck model (CBM) designed for interpretable lung cancer diagnosis. We now extend that approach and present XpertXAI, a generalizable expert-driven model that preserves human-interpretable clinical concepts while scaling to detect multiple lung pathologies. Using a high-performing InceptionV3-based classifier and a public dataset of chest X-rays with radiology reports, we compare XpertXAI against leading post-hoc explainability methods and an unsupervised CBM, XCBs. We assess explanations through comparison with expert radiologist annotations and medical ground truth. Although XpertXAI is trained for multiple pathologies, our expert validation focuses on lung cancer. We find that existing techniques frequently fail to produce clinically meaningful explanations, omitting key diagnostic features and disagreeing with radiologist judgments. XpertXAI not only outperforms these baselines in predictive accuracy but also delivers concept-level explanations that better align with expert reasoning. While our focus remains on explainability in lung cancer detection, this work illustrates how human-centric model design can be effectively extended to broader diagnostic contexts - offering a scalable path toward clinically meaningful explainable AI in medical diagnostics.

Related papers

• RadFabric: Agentic AI System with Reasoning Capability for Radiology [61.25593938175618]
  RadFabric is a multi agent, multimodal reasoning framework that unifies visual and textual analysis for comprehensive CXR interpretation.<n>System employs specialized CXR agents for pathology detection, an Anatomical Interpretation Agent to map visual findings to precise anatomical structures, and a Reasoning Agent powered by large multimodal reasoning models to synthesize visual, anatomical, and clinical data into transparent and evidence based diagnoses.
  arXiv  Detail & Related papers  (2025-06-17T03:10:33Z)
• A Narrative Review on Large AI Models in Lung Cancer Screening, Diagnosis, and Treatment Planning [8.431488361911754]
  Lung cancer remains one of the most prevalent and fatal diseases worldwide.<n>Recent advancements in large AI models have significantly enhanced medical image understanding and clinical decision-making.<n>This review systematically surveys the state-of-the-art in applying large AI models to lung cancer screening, diagnosis, prognosis, and treatment.
  arXiv  Detail & Related papers  (2025-06-08T17:42:24Z)
• Medical AI for Early Detection of Lung Cancer: A Survey [11.90341994990241]
  Lung cancer remains one of the leading causes of morbidity and mortality worldwide. Computer-aided diagnosis (CAD) systems have proven effective in detecting and classifying pulmonary nodules. Deep learning algorithms have markedly improved the accuracy and efficiency of pulmonary nodule analysis.
  arXiv  Detail & Related papers  (2024-10-18T17:45:42Z)
• Leveraging Expert Input for Robust and Explainable AI-Assisted Lung Cancer Detection in Chest X-rays [2.380494879018844]
  This study examines the interpretability and robustness of a high-performing lung cancer detection model based on InceptionV3.<n>We develop ClinicXAI, an expert-driven approach leveraging the concept bottleneck methodology.
  arXiv  Detail & Related papers  (2024-03-28T14:15:13Z)
• Expert Uncertainty and Severity Aware Chest X-Ray Classification by Multi-Relationship Graph Learning [48.29204631769816]
  We re-extract disease labels from CXR reports to make them more realistic by considering disease severity and uncertainty in classification. Our experimental results show that models considering disease severity and uncertainty outperform previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-09-06T19:19:41Z)
• Deep Reinforcement Learning Framework for Thoracic Diseases Classification via Prior Knowledge Guidance [49.87607548975686]
  The scarcity of labeled data for related diseases poses a huge challenge to an accurate diagnosis. We propose a novel deep reinforcement learning framework, which introduces prior knowledge to direct the learning of diagnostic agents. Our approach's performance was demonstrated using the well-known NIHX-ray 14 and CheXpert datasets.
  arXiv  Detail & Related papers  (2023-06-02T01:46:31Z)
• BI-RADS-Net: An Explainable Multitask Learning Approach for Cancer Diagnosis in Breast Ultrasound Images [69.41441138140895]
  This paper introduces BI-RADS-Net, a novel explainable deep learning approach for cancer detection in breast ultrasound images. The proposed approach incorporates tasks for explaining and classifying breast tumors, by learning feature representations relevant to clinical diagnosis. Explanations of the predictions (benign or malignant) are provided in terms of morphological features that are used by clinicians for diagnosis and reporting in medical practice.
  arXiv  Detail & Related papers  (2021-10-05T19:14:46Z)
• CoRSAI: A System for Robust Interpretation of CT Scans of COVID-19 Patients Using Deep Learning [133.87426554801252]
  We adopted an approach based on using an ensemble of deep convolutionalneural networks for segmentation of lung CT scans. Using our models we are able to segment the lesions, evaluatepatients dynamics, estimate relative volume of lungs affected by lesions and evaluate the lung damage stage.
  arXiv  Detail & Related papers  (2021-05-25T12:06:55Z)
• Variational Knowledge Distillation for Disease Classification in Chest X-Rays [102.04931207504173]
  We propose itvariational knowledge distillation (VKD), which is a new probabilistic inference framework for disease classification based on X-rays. We demonstrate the effectiveness of our method on three public benchmark datasets with paired X-ray images and EHRs.
  arXiv  Detail & Related papers  (2021-03-19T14:13:56Z)
• Automatic Generation of Interpretable Lung Cancer Scoring Models from Chest X-Ray Images [9.525711971667679]
  Lung cancer is the leading cause of cancer death worldwide. Deep learning techniques are effective at automatically diagnosing lung cancer. These techniques have yet to be clinically approved and adopted by the medical community.
  arXiv  Detail & Related papers  (2020-12-10T04:11:59Z)
• Deep Learning for Automatic Pneumonia Detection [72.55423549641714]
  Pneumonia is the leading cause of death among young children and one of the top mortality causes worldwide. Computer-aided diagnosis systems showed the potential for improving diagnostic accuracy. We develop the computational approach for pneumonia regions detection based on single-shot detectors, squeeze-and-excitation deep convolution neural networks, augmentations and multi-task learning.
  arXiv  Detail & Related papers  (2020-05-28T10:54:34Z)
• Attention U-Net Based Adversarial Architectures for Chest X-ray Lung Segmentation [0.0]
  We present a novel deep learning approach for lung segmentation, a basic, but arduous task in the diagnostic pipeline. Our method uses state-of-the-art fully convolutional neural networks in conjunction with an adversarial critic model. It generalized well to CXR images of unseen datasets with different patient profiles, achieving a final DSCR of 97.5% on the JSRT dataset.
  arXiv  Detail & Related papers  (2020-03-23T14:45:48Z)

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.