5 papers with code • 1 benchmarks • 0 datasets
Medical object detection is the task of identifying medical-based objects within an image.
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.
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.
Ranked #1 on Medical Object Detection on Barrett’s Esophagus
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).
Benchmarking of novel methods can provide a direction to the development of automated polyp detection and segmentation tasks.
Ranked #4 on Medical Image Segmentation on CVC-ClinicDB
At 8 false positives per image, we detect 92. 4% of the tumors, relative to 82. 7% by the previous best automated approach.
Ranked #2 on Medical Object Detection on Barrett’s Esophagus