Papers with Code · CC BY-SA 4.0

Medical Object Detection

3D › Object Detection › Medical Object Detection 14 평가 기록 4 datasets

Medical object detection is the task of identifying medical-based objects within an image.

<span style="color:grey; opacity: 0.6">( Image credit: Liver Lesion Detection from Weakly-labeled Multi-phase CT Volumes with a Grouped Single Shot MultiBox Detector )</span>

논문

3D Context Enhanced Region-based Convolutional Neural Network for End-to-End Lesion Detection

1806.09648 2018-06-25 이 페이지의 평가 1건

Sensitivity: 75.55

Advancing 3D Medical Image Analysis with Variable Dimension Transform based Supervised 3D Pre-training

2201.01426 2022-01-05 이 페이지의 평가 1건

Sensitivity: 88.55

An Efficient Anchor-free Universal Lesion Detection in CT-scans

2203.16074 2022-03-30 이 페이지의 평가 1건

Sensitivity: 86.05

Attention-Based Deep Neural Networks for Detection of Cancerous and Precancerous Esophagus Tissue on Histopathological Slides

1811.08513 2018-11-20 이 페이지의 평가 1건

Mean Accuracy: 81%

Circle Representation for Medical Object Detection

2110.12093 2021-10-22 이 페이지의 평가 1건

Average-mAP: 0.487

Conditional Training with Bounding Map for Universal Lesion Detection

2103.12277 2021-03-23 이 페이지의 평가 1건

Sensitivity: 86.83

Detecting Cancer Metastases on Gigapixel Pathology Images

1703.02442 2017-03-03 이 페이지의 평가 1건

Mean Accuracy: 74%

DKMA-ULD: Domain Knowledge augmented Multi-head Attention based Robust Universal Lesion Detection

2203.06886 2022-03-14 이 페이지의 평가 1건

Sensitivity: 87.16

Enhancing Wrist Fracture Detection with YOLO

2407.12597 2024-07-17 이 페이지의 평가 1건

mAP: 77.00

Improving RetinaNet for CT Lesion Detection with Dense Masks from Weak RECIST Labels

1906.02283 2019-06-05 이 페이지의 평가 1건

Sensitivity: 82.36

MULAN: Multitask Universal Lesion Analysis Network for Joint Lesion Detection, Tagging, and Segmentation

1908.04373 2019-08-12 이 페이지의 평가 1건

Sensitivity: 85.22

MVP-Net: Multi-view FPN with Position-aware Attention for Deep Universal Lesion Detection

1909.04247 2019-09-10 이 페이지의 평가 1건

Sensitivity: 83.64

Revisiting 3D Context Modeling with Supervised Pre-training for Universal Lesion Detection in CT Slices

2012.08770 2020-12-16 이 페이지의 평가 1건

Sensitivity: 86.74

Universal Lesion Detection by Learning from Multiple Heterogeneously Labeled Datasets

2005.13753 2020-05-28 이 페이지의 평가 1건

Sensitivity: 86.6