Medical Object Detection
27 papers with code • 3 benchmarks • 3 datasets
Medical object detection is the task of identifying medical-based objects within an image.
( Image credit: Liver Lesion Detection from Weakly-labeled Multi-phase CT Volumes with a Grouped Single Shot MultiBox Detector )
Latest papers
YOLOv9 for Fracture Detection in Pediatric Wrist Trauma X-ray Images
The introduction of YOLOv9, the latest version of the You Only Look Once (YOLO) series, has led to its widespread adoption across various scenarios.
YOLOv8-AM: YOLOv8 with Attention Mechanisms for Pediatric Wrist Fracture Detection
Specifically, we respectively employ four attention modules, Convolutional Block Attention Module (CBAM), Global Attention Mechanism (GAM), Efficient Channel Attention (ECA), and Shuffle Attention (SA), to design the improved models and train them on GRAZPEDWRI-DX dataset.
BGF-YOLO: Enhanced YOLOv8 with Multiscale Attentional Feature Fusion for Brain Tumor Detection
You Only Look Once (YOLO)-based object detectors have shown remarkable accuracy for automated brain tumor detection.
CircleFormer: Circular Nuclei Detection in Whole Slide Images with Circle Queries and Attention
Inspired by the recent anchor free CNN-based circular object detection method (CircleNet) for ball-shape glomeruli detection in renal pathology, in this paper, we present CircleFormer, a Transformer-based circular medical object detection with dynamic anchor circles.
RCS-YOLO: A Fast and High-Accuracy Object Detector for Brain Tumor Detection
With an excellent balance between speed and accuracy, cutting-edge YOLO frameworks have become one of the most efficient algorithms for object detection.
CST-YOLO: A Novel Method for Blood Cell Detection Based on Improved YOLOv7 and CNN-Swin Transformer
Blood cell detection is a typical small-scale object detection problem in computer vision.
nnDetection for Intracranial Aneurysms Detection and Localization
Intracranial aneurysms are a commonly occurring and life-threatening condition, affecting approximately 3. 2% of the general population.
Fracture Detection in Pediatric Wrist Trauma X-ray Images Using YOLOv8 Algorithm
To enable surgeons to use our model for fracture detection on pediatric wrist trauma X-ray images, we have designed the application "Fracture Detection Using YOLOv8 App" to assist surgeons in diagnosing fractures, reducing the probability of error analysis, and providing more useful information for surgery.
DeGPR: Deep Guided Posterior Regularization for Multi-Class Cell Detection and Counting
While there exist multiple, general-purpose, deep learning-based object detection and counting methods, they may not readily transfer to detecting and counting cells in medical images, due to the limited data, presence of tiny overlapping objects, multiple cell types, severe class-imbalance, minute differences in size/shape of cells, etc.
Roboflow 100: A Rich, Multi-Domain Object Detection Benchmark
The evaluation of object detection models is usually performed by optimizing a single metric, e. g. mAP, on a fixed set of datasets, e. g. Microsoft COCO and Pascal VOC.