Medical Object Detection

14 papers with code • 4 benchmarks • 2 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 )

Most implemented papers

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

rsummers11/CADLab 12 Aug 2019

When reading medical images such as a computed tomography (CT) scan, radiologists generally search across the image to find lesions, characterize and measure them, and then describe them in the radiological report.

Detecting Cancer Metastases on Gigapixel Pathology Images

Reemr/Cancer-detection 3 Mar 2017

At 8 false positives per image, we detect 92. 4% of the tumors, relative to 82. 7% by the previous best automated approach.

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

pfjaeger/medicaldetectiontoolkit 21 Nov 2018

The proposed architecture recaptures discarded supervision signals by complementing object detection with an auxiliary task in the form of semantic segmentation without introducing the additional complexity of previously proposed two-stage detectors.

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

fizyr/keras-retinanet 5 Jun 2019

We propose a highly accurate and efficient one-stage lesion detector, by re-designing a RetinaNet to meet the particular challenges in medical imaging.

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

L0SG/grouped-ssd-pytorch 2 Jul 2018

We present a focal liver lesion detection model leveraged by custom-designed multi-phase computed tomography (CT) volumes, which reflects real-world clinical lesion detection practice using a Single Shot MultiBox Detector (SSD).

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

BMIRDS/deepslide 20 Nov 2018

Deep learning-based methods, such as the sliding window approach for cropped-image classification and heuristic aggregation for whole-slide inference, for analyzing histological patterns in high-resolution microscopy images have shown promising results.

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

urmagicsmine/MVP-Net 10 Sep 2019

In this paper, we propose to incorporate domain knowledge in clinical practice into the model design of universal lesion detectors.

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

DebeshJha/ColonSegNet 15 Nov 2020

Benchmarking of novel methods can provide a direction to the development of automated polyp detection and segmentation tasks.

nnDetection: A Self-configuring Method for Medical Object Detection

MIC-DKFZ/nnDetection 1 Jun 2021

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.