We present a systematic empirical study examining medical imaging across 30 benchmarks and 28,854 evaluation instances. Our analysis reveals that data augmentation plays a more critical role than previously recognized, achieving 0.
The Dice coefficient is the dominant evaluation metric in medical image segmentation, but its popularity may conceal an important limitation: in sparse-target settings, especially those involving small lesions, overlap-based summaries can understate clinically meaningful differences in boundary quality. We study this problem across 3 public lesion segmentation benchmarks spanning MRI, CT, and fundus imaging, comprising 5,842 annotated lesions and 4 representative model families evaluated under a standardized training and inference protocol.
Evaluate 3 segmentation models (nnU-Net, Swin-UNETR, TransUNet) on 4 organs (liver, kidney, pancreas, spleen) from Medical Segmentation Decathlon. Compute Dice, 95th-percentile Hausdorff Distance (HD95), Average Surface Distance (ASD), and Normalized Surface Dice (NSD).
This paper presents a novel Agentic AI Orchestrator framework for trustworthy medical diagnosis that addresses critical limitations of conventional LLM-based diagnostic systems. Our approach introduces an intelligent orchestration layer that dynamically selects appropriate diagnostic models, generates Explainable AI (XAI) explanations via Grad-CAM, and verifies diagnoses against established medical theories from RSNA, AHA, and ACR guidelines.
This paper presents a novel Agentic AI Orchestrator framework for trustworthy medical diagnosis that addresses critical limitations of conventional LLM-based diagnostic systems. Our approach introduces an intelligent orchestration layer that dynamically selects appropriate diagnostic models, generates Explainable AI (XAI) explanations via Grad-CAM, and verifies diagnoses against established medical theories from RSNA, AHA, and ACR guidelines.
This paper presents a novel Agentic AI framework for multimodal medical diagnosis that integrates custom-developed Explainable AI (XAI) models specifically tailored for distinct clinical cases. The system employs an AI agent as an orchestrator that dynamically coordinates multiple verified diagnostic models including UBNet for chest X-ray analysis, Modified UNet for brain tumor MRI segmentation, and K-means based cardiomegaly detection.