mlsanityData to 7 Oct 2026

Object Detection for Medical Image Analysis: Insights from the RT-DETR Model

arXiv 2501.16469 ↗First posted 27 Jan 2025cs.CV cs.LGcited by 10

Not on the map. Why?

Abstract (arXiv)

Deep learning has emerged as a transformative approach for solving complex pattern recognition and object detection challenges. This paper focuses on the application of a novel detection framework based on the RT-DETR model for analyzing intricate image data, particularly in areas such as diabetic retinopathy detection. Diabetic retinopathy, a leading cause of vision loss globally, requires accurate and efficient image analysis to identify early-stage lesions. The proposed RT-DETR model, built on a Transformer-based architecture, excels at processing high-dimensional and complex visual data with enhanced robustness and accuracy. Comparative evaluations with models such as YOLOv5, YOLOv8, SSD, and DETR demonstrate that RT-DETR achieves superior performance across precision, recall, mAP50, and mAP50-95 metrics, particularly in detecting small-scale objects and densely packed targets. This study underscores the potential of Transformer-based models like RT-DETR for advancing object detection tasks, offering promising applications in medical imaging and beyond.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 5 of its 22 references that are in the map. First posted on arXiv 27 Jan 2025.

Builds on

5 of 22 references.

References · 5

  1. 2020Deformable DETR: Deformable Transformers for End-to-End Object Detectioncited by 3,410
  2. 2015SSD: Single Shot MultiBox Detectorcited by 3,125
  3. 2024Convolutional neural network classification of cancer cytopathology images: taking breast cancer as an examplecited by 50
  4. 2024Few-Shot Learning with Adaptive Weight Masking in Conditional GANscited by 4
  5. 2024Optimizing Large Language Models with an Enhanced LoRA Fine-Tuning Algorithm for Efficiency and Robustness in NLP Taskscited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 9; 2026: 1.

10 papers.

  1. 2025Graph Neural Network-Driven Hierarchical Mining for Complex Imbalanced Datacited by 11
  2. 2025Dynamic Operating System Scheduling Using Double DQN: A Reinforcement Learning Approach to Task Optimizationcited by 10
  3. 2025Optimized Unet with Attention Mechanism for Multi-Scale Semantic Segmentationcited by 9
  4. 2025Context-Aware Rule Mining Using a Dynamic Transformer-Based Frameworkcited by 7
  5. 2025A Fine-Tuning Approach for T5 Using Knowledge Graphs to Address Complex Taskscited by 6
  6. 2025A LongFormer-Based Framework for Accurate and Efficient Medical Text Summarizationcited by 5
  7. 2025Pre-trained Language Models and Few-shot Learning for Medical Entity Extractioncited by 4
  8. 2025MedROV: Towards Real-Time Open-Vocabulary Detection Across Diverse Medical Imaging Modalitiescited by 2
  9. 2025Object-Centric Data Synthesis for Category-level Object Detectioncited by 0
  10. 2026Nodule-DETR: A Novel DETR Architecture with Frequency-Channel Attention for Ultrasound Thyroid Nodule Detectioncited by 0