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 )
Benchmarks
These leaderboards are used to track progress in Medical Object Detection
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DeepLesion
SKM-TEA
RF100
Most implemented papers
Revisiting 3D Context Modeling with Supervised Pre-training for Universal Lesion Detection in CT Slices
We demonstrate that with the novel pre-training method, the proposed MP3D FPN achieves state-of-the-art detection performance on the DeepLesion dataset (3. 48% absolute improvement in the sensitivity of FPs@0. 5), significantly surpassing the baseline method by up to 6. 06% (in MAP@0. 5) which adopts 2D convolution for 3D context modeling.
nnDetection: A Self-configuring Method for Medical Object Detection
Simultaneous localisation and categorization of objects in medical images, also referred to as medical object detection, is of high clinical relevance because diagnostic decisions often depend on rating of objects rather than e. g. pixels.
Circle Representation for Medical Object Detection
Compared with the conventional bounding box representation, the proposed bounding circle representation innovates in three-fold: (1) it is optimized for ball-shaped biomedical objects; (2) The circle representation reduced the degree of freedom compared with box representation; (3) It is naturally more rotation invariant.
Fracture Detection in Wrist X-ray Images Using Deep Learning-Based Object Detection Models
The aim of this study is to perform fracture detection by use of deep learning on wrist Xray images to support physicians in the diagnosis of these fractures, particularly in the emergency services.
Advancing 3D Medical Image Analysis with Variable Dimension Transform based Supervised 3D Pre-training
The difficulties in both data acquisition and annotation substantially restrict the sample sizes of training datasets for 3D medical imaging applications.
Transformers in Medical Imaging: A Survey
fahadshamshad/awesome-transformers-in-medical-imaging
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Following unprecedented success on the natural language tasks, Transformers have been successfully applied to several computer vision problems, achieving state-of-the-art results and prompting researchers to reconsider the supremacy of convolutional neural networks (CNNs) as {de facto} operators.
Automatic Acne Object Detection and Acne Severity Grading Using Smartphone Images and Artificial Intelligence
HuynhThanhQuan/skin-detective
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Diagnostics 2022
AcneDet includes two models for two tasks: (1) a Faster R-CNN-based deep learning model for the detection of acne lesion objects of four types, including blackheads/whiteheads, papules/pustules, nodules/cysts, and acne scars; and (2) a LightGBM machine learning model for grading acne severity using the Investigator’s Global Assessment (IGA) scale.
Roboflow 100: A Rich, Multi-Domain Object Detection Benchmark
roboflow-ai/roboflow-100-benchmark
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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.
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.
Fracture Detection in Pediatric Wrist Trauma X-ray Images Using YOLOv8 Algorithm
ruiyangju/bone_fracture_detection_yolov8
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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.
