Experience-Guided Self-Adaptive Cascaded Agents for Breast Cancer Screening and Diagnosis with Reduced Biopsy Referrals

• URL: http://arxiv.org/abs/2602.23899v1
• Date: Fri, 27 Feb 2026 10:48:14 GMT
• Title: Experience-Guided Self-Adaptive Cascaded Agents for Breast Cancer Screening and Diagnosis with Reduced Biopsy Referrals
• Abstract summary: Our framework models screening and diagnosis as a two-stage, selective decision-making process.<n>A lightweight screening clinic' agent selectively filters out benign and normal cases from further diagnostic escalation.<n>A diagnostic clinic' agent integrates richer perception and radiological description tools to make a secondary decision on biopsy referral.
• Score: 8.077581449917155
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose an experience-guided cascaded multi-agent framework for Breast Ultrasound Screening and Diagnosis, called BUSD-Agent, that aims to reduce diagnostic escalation and unnecessary biopsy referrals. Our framework models screening and diagnosis as a two-stage, selective decision-making process. A lightweight `screening clinic' agent, restricted to classification models as tools, selectively filters out benign and normal cases from further diagnostic escalation when malignancy risk and uncertainty are estimated as low. Cases that have higher risks are escalated to the `diagnostic clinic' agent, which integrates richer perception and radiological description tools to make a secondary decision on biopsy referral. To improve agent performance, past records of pathology-confirmed outcomes along with image embeddings, model predictions, and historical agent actions are stored in a memory bank as structured decision trajectories. For each new case, BUSD-Agent retrieves similar past cases based on image, model response and confidence similarity to condition the agent's current decision policy. This enables retrieval-conditioned in-context adaptation that dynamically adjusts model trust and escalation thresholds from prior experiences without parameter updates. Evaluation across 10 breast ultrasound datasets shows that the proposed experience-guided workflow reduces diagnostic escalation in BUSD-Agent from 84.95% to 58.72% and overall biopsy referrals from 59.50% to 37.08%, compared to the same architecture without trajectory conditioning, while improving average screening specificity by 68.48% and diagnostic specificity by 6.33%.

Related papers

• FRAC-MAS: A Safe and Explainable Multi-Agent System for Fracture Diagnosis [0.6351973754158782]
  FRAC-MAS is an agentic AI system for automated, explainable, and safe bone fracture detection.<n>A pipeline-depth ablation study confirms that our multi-agent critic triages 86.6% of cases into a high-confidence auto-confirmed cohort.<n>Patient preference studies against Llama, MedGemma, and Gemini demonstrate significantly more comprehensible clinical reports.
  arXiv  Detail & Related papers  (2026-08-22T10:56:00Z)
• Auditable agentic AI for evidence-grounded thyroid ultrasound diagnosis and reporting [59.709162055944915]
  ThyroidXAgent is an agentic AI system that coordinates specialized diagnostic tools and stores their outputs as an auditable case-level evidence record.<n>It was evaluated on 28,458 non-overlapping test cases, including 8,721 cases from 35 centres in the private NHC-MISD-TUS cohort.<n>It achieved a mean Dice score of 87.21 percent for segmentation and a mean AUROC of 0.9466 for benign-malignant classification.
  arXiv  Detail & Related papers  (2026-08-12T21:02:59Z)
• Evaluating the Diagnostic Robustness of Vision-Language Models Under Visual and Textual Perturbations [3.765860543344957]
  We assess the diagnostic robustness of four VLM families under evidence-preserving perturbations.<n>Our results reveal significant vulnerabilities in presentation-order stability, with models exhibiting prediction flips in up to 48.9% of cases.
  arXiv  Detail & Related papers  (2026-08-05T14:09:55Z)
• SAGEAgent: A Self-Evolving Agent for Cost-Aware Modality Acquisition in Multimodal Survival Prediction [6.185729888403738]
  We propose SAGEAgent, a self-evolving clinical agent that decides which diagnostic modalities to acquire for each patient.<n> Experiments show SAGEAgent achieves competitive survival prediction accuracy while reducing average acquisition burden by 55%.
  arXiv  Detail & Related papers  (2026-07-10T15:32:11Z)
• Guideline-Grounded Evidence Accumulation for High-Stakes Agent Verification [60.18369393468405]
  Existing verifiers usually underperform owing to a lack of domain knowledge and limited calibration.<n>GLEAN compiles expert-curated protocols into trajectory-informed, well-calibrated correctness signals.<n>We empirically validate GLEAN with agentic clinical diagnosis across three diseases from the MIMIC-IV dataset.
  arXiv  Detail & Related papers  (2026-03-03T09:36:43Z)
• AgentsEval: Clinically Faithful Evaluation of Medical Imaging Reports via Multi-Agent Reasoning [73.50200033931148]
  We introduce AgentsEval, a multi-agent stream reasoning framework that emulates the collaborative diagnostic workflow of radiologists.<n>By dividing the evaluation process into interpretable steps including criteria definition, evidence extraction, alignment, and consistency scoring, AgentsEval provides explicit reasoning traces and structured clinical feedback.<n> Experimental results demonstrate that AgentsEval delivers clinically aligned, semantically faithful, and interpretable evaluations that remain robust under paraphrastic, semantic, and stylistic perturbations.
  arXiv  Detail & Related papers  (2026-01-23T11:59:13Z)
• A DeepSeek-Powered AI System for Automated Chest Radiograph Interpretation in Clinical Practice [83.11942224668127]
  Janus-Pro-CXR (1B) is a chest X-ray interpretation system based on DeepSeek Janus-Pro model.<n>Our system outperforms state-of-the-art X-ray report generation models in automated report generation.
  arXiv  Detail & Related papers  (2025-12-23T13:26:13Z)
• Evolving Diagnostic Agents in a Virtual Clinical Environment [75.59389103511559]
  We present a framework for training large language models (LLMs) as diagnostic agents with reinforcement learning.<n>Our method acquires diagnostic strategies through interactive exploration and outcome-based feedback.<n>DiagAgent significantly outperforms 10 state-of-the-art LLMs, including DeepSeek-v3 and GPT-4o.
  arXiv  Detail & Related papers  (2025-10-28T17:19:47Z)
• An Explainable Hybrid AI Framework for Enhanced Tuberculosis and Symptom Detection [55.35661671061754]
  Tuberculosis remains a critical global health issue, particularly in resource-limited and remote areas.<n>We propose a framework which enhances disease and symptom detection on chest X-rays by integrating two supervised heads and a self-supervised head.<n>Our model achieves an accuracy of 98.85% for distinguishing between COVID-19, tuberculosis, and normal cases, and a macro-F1 score of 90.09% for multilabel symptom detection.
  arXiv  Detail & Related papers  (2025-10-21T17:18:55Z)
• Multi-pathology Chest X-ray Classification with Rejection Mechanisms [36.0596663889937]
  Overconfidence in deep learning models poses a significant risk in high-stakes medical imaging tasks.<n>This study introduces an uncertainty-aware framework for chest X-ray diagnosis based on a DenseNet-121 backbone.
  arXiv  Detail & Related papers  (2025-09-12T15:36:26Z)
• 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)
• Uncertainty Estimation for Trust Attribution to Speed-of-Sound Reconstruction with Variational Networks [4.4514169794746]
  Speed-of-sound (SoS) is a biomechanical characteristic of tissue, and its imaging can provide a promising biomarker for diagnosis.<n>We propose to use the uncertainty in SoS reconstructions to trust attribute to each individual acquired frame.<n>We assess our automatic frame selection method for differential diagnosis of breast cancer, distinguishing between benign fibroadenoma and malignant carcinoma.
  arXiv  Detail & Related papers  (2025-04-15T15:48:51Z)
• A Synthetic Data-Driven Radiology Foundation Model for Pan-tumor Clinical Diagnosis [21.212976987658415]
  PASTA is a pan-tumor radiology foundation model built on PASTA-Gen.<n> PASTA achieves state-of-the-art performance on 45 of 46 oncology tasks.
  arXiv  Detail & Related papers  (2025-02-10T05:45:03Z)
• PersonalizedUS: Interpretable Breast Cancer Risk Assessment with Local Coverage Uncertainty Quantification [2.6911061523689415]
  The current "golden standard" relies on manual BI-RADS scoring by clinicians, often leading to unnecessary biopsies and a significant mental health burden on patients and their families. We introduce PersonalizedUS, an interpretable machine learning system that leverages recent advances in conformal prediction to provide precise and personalized risk estimates. Concrete clinical benefits include up to a 65% reduction in requested biopsies among BI-RADS 4a and 4b lesions, with minimal to no missed cancer cases.
  arXiv  Detail & Related papers  (2024-08-28T00:47:55Z)
• Exploring linguistic feature and model combination for speech recognition based automatic AD detection [61.91708957996086]
  Speech based automatic AD screening systems provide a non-intrusive and more scalable alternative to other clinical screening techniques. Scarcity of specialist data leads to uncertainty in both model selection and feature learning when developing such systems. This paper investigates the use of feature and model combination approaches to improve the robustness of domain fine-tuning of BERT and Roberta pre-trained text encoders.
  arXiv  Detail & Related papers  (2022-06-28T05:09:01Z)
• Multi-Label Classification of Thoracic Diseases using Dense Convolutional Network on Chest Radiographs [0.0]
  We propose a multi-label disease prediction model that allows the detection of more than one pathology at a given test time. Our proposed model achieved the highest AUC score of 0.896 for the condition Cardiomegaly.
  arXiv  Detail & Related papers  (2022-02-08T00:43:57Z)

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.