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 with no code
ADA-YOLO: Dynamic Fusion of YOLOv8 and Adaptive Heads for Precise Image Detection and Diagnosis
To address this issue, we propose ADA-YOLO, a light-weight yet effective method for medical object detection that integrates attention-based mechanisms with the YOLOv8 architecture.
Taming Detection Transformers for Medical Object Detection
The accurate detection of suspicious regions in medical images is an error-prone and time-consuming process required by many routinely performed diagnostic procedures.
Accurate Detection of Mediastinal Lesions with nnDetection
The accurate detection of mediastinal lesions is one of the rarely explored medical object detection problems.
An Efficient Anchor-free Universal Lesion Detection in CT-scans
Existing universal lesion detection (ULD) methods utilize compute-intensive anchor-based architectures which rely on predefined anchor boxes, resulting in unsatisfactory detection performance, especially in small and mid-sized lesions.
DKMA-ULD: Domain Knowledge augmented Multi-head Attention based Robust Universal Lesion Detection
In this paper, we exploit the domain information present in computed tomography (CT) scans and propose a robust universal lesion detection (ULD) network that can detect lesions across all organs of the body by training on a single dataset, DeepLesion.
Lightweight Transformer Backbone for Medical Object Detection
Specifically, we propose a novel modification of visual transformer (ViT) on image feature patches to connect the feature patches of a tumor with healthy backgrounds of breast images and form a more robust backbone for tumor detection.
SpineOne: A One-Stage Detection Framework for Degenerative Discs and Vertebrae
Spinal degeneration plagues many elders, office workers, and even the younger generations.
Conditional Training with Bounding Map for Universal Lesion Detection
Universal Lesion Detection (ULD) in computed tomography plays an essential role in computer-aided diagnosis.
MACD R-CNN: An Abnormal Cell Nucleus Detection Method
We design a fixed proposal module to generate fixed-sized feature maps of nuclei, which allows the new information of nucleus is used for classification.
Universal Lesion Detection by Learning from Multiple Heterogeneously Labeled Datasets
First, we learn a multi-head multi-task lesion detector using all datasets and generate lesion proposals on DeepLesion.