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object-detection

Medical Object Detection

3260 papers • 126 benchmarks • 313 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 )

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in object-detection

Trend

Dataset

Best Model

Actions

DeepLesion

DeepLesion

Barrett’s Esophagus

Barrett’s Esophagus

MoNuSeg 2018

Libraries

i

Use these libraries to find object-detection models and implementations

Datasets

DeepLesion

SKM-TEA

GRAZPEDWRI-DX

RF100

Subtasks

No subtasks available.

Most implemented papers

Detecting Cancer Metastases on Gigapixel Pathology Images

Yun Liu, L. Peng, G. Corrado, Mohammad Norouzi, Martin C. Stumpe, Subhashini Venugopalan, George E. Dahl, Aleksei Timofeev, Krishna Gadepalli, Timo Kohlberger, A. Boyko, Phil Q. Nelson, J. Hipp•Thu Mar 02 2017

This work presents a framework to automatically detect and localize tumors as small as 100 x 100 pixels in gigapixel microscopy images sized 100,000 x100,000 pixels and achieves image-level AUC scores above 97% on both the Camelyon16 test set and an independent set of 110 slides.

Content

Introduction Benchmarks Datasets Subtasks Libraries Papers

MoNuSeg 2018

GRAZPEDWRI-DX

GRAZPEDWRI-DX

668

0

Retina U-Net: Embarrassingly Simple Exploitation of Segmentation Supervision for Medical Object Detection

Fabian Isensee, P. Jaeger, Simon A. A. Kohl, Klaus Hermann Maier-Hein, S. Bickelhaupt, T. Kuder, H. Schlemmer•Tue Nov 20 2018

Retina U-Net is proposed, a simple architecture, which naturally fuses the Retina Net one-stage detector with the U- net architecture widely used for semantic segmentation in medical images and yields strong detection performance only reached by its more complex two-staged counterparts.

216 0

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

R. Summers, Zhiyong Lu, Yifan Peng, M. Bagheri, K. Yan, Youbao Tang, V. Sandfort•Sun Aug 11 2019

A multitask universal lesion analysis network (MULAN) for joint detection, tagging, and segmentation of lesions in a variety of body parts, which greatly extends existing work of single-task lesions analysis on specific body parts.

91 0

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

R. Summers, M. Bagheri, Ke Yan•Sun Jun 24 2018

3D context enhanced region-based CNN (3DCE) is proposed to incorporate 3D context information efficiently by aggregating feature maps of 2D images to detect lesions from computed tomography scans.

127 0

Robust End-to-End Focal Liver Lesion Detection Using Unregistered Multiphase Computed Tomography Images

Sungroh Yoon, Eunji Kim, Sang-gil Lee, J. S. Bae, J. H. Kim•Wed Dec 01 2021

Evaluation on a large-scale dataset with 280 patients confirmed that the proposed method outperformed previous state-of-the-art methods and significantly reduced the performance degradation for detecting FLLs using misaligned multiphase CT images.

11 0

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

A. Suriawinata, Bing Ren, Naofumi Tomita, S. Hassanpour, B. Abdollahi, Jason Wei•Mon Nov 19 2018

This diagnostic study describes a novel attention-based deep neural network framework for classifying microscopy images to identify Barrett esophagus and esophageal adenocarcinoma.

179 0

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

Yizhou Yu, Yizhou Wang, Kaiqi Huang, Junge Zhang, Shu Zhang, Zihao Li•Mon Sep 09 2019

With the proposed model design, the data-hunger problem is relieved as the learning task is made easier with the correctly induced clinical practice prior, and promising results are shown on the NIH DeepLesion dataset.

76 0

Liver Lesion Detection from Weakly-labeled Multi-phase CT Volumes with a Grouped Single Shot MultiBox Detector

Sungroh Yoon, Sang-gil Lee, J. S. Bae, J. H. Kim, Hyunjae Kim•Sun Jul 01 2018

Results show that the one-stage object detection model is a practical solution, which runs in near real-time and can learn an unbiased feature representation from a large-volume real-world detection dataset, which requires less tedious and time consuming construction of the weak phase-level bounding box labels.

33 0

Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learning

Debesh Jha, P. Halvorsen, Dag Johansen, M. Riegler, Sharib Ali, H. Johansen, Nikhil Kumar Tomar, J. Rittscher•Wed Mar 03 2021

A comprehensive comparison with various state-of-the-art methods reveals the importance of benchmarking the deep learning methods for automated real-time polyp identification and delineations that can potentially transform current clinical practices and minimise miss-detection rates.

257 0

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

Q. Dou, Ben Glocker, Martin Zlocha•Tue Jun 04 2019

This work proposes a highly accurate and efficient one-stage lesion detector, by re-designing a RetinaNet to meet the particular challenges in medical imaging, and optimize the anchor configurations using a differential evolution search algorithm.

115 0

Adding a benchmark result helps the community track progress.

Medical Object Detection | State-of-the-Art