Medical Object Detection

5 papers with code • 1 benchmarks • 0 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 )

Datasets


Greatest papers with code

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.

Medical Object Detection Semantic Segmentation +1

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.

Crop Classification General Classification +2

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).

Computed Tomography (CT) Lesion Segmentation +1

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.

Medical Object Detection