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Found 7 papers, 4 papers with code
OCELOT: Overlapped Cell on Tissue Dataset for Histopathology
Cell detection is a fundamental task in computational pathology that can be used for extracting high-level medical information from whole-slide images.
Benchmarking Self-Supervised Learning on Diverse Pathology Datasets
To address this need, we execute the largest-scale study of SSL pre-training on pathology image data, to date.
Interactive Multi-Class Tiny-Object Detection
We perform experiments on the Tiny-DOTA and LCell datasets using both two-stage and one-stage object detection architectures to verify the efficacy of our approach.
Multi-stage Deep Layer Aggregation for Brain Tumor Segmentation
Gliomas are among the most aggressive and deadly brain tumors.
Combining unsupervised and supervised learning for predicting the final stroke lesion
Predicting the final ischaemic stroke lesion provides crucial information regarding the volume of salvageable hypoperfused tissue, which helps physicians in the difficult decision-making process of treatment planning and intervention.
Retinal vessel segmentation based on Fully Convolutional Neural Networks
The retinal vascular condition is a reliable biomarker of several ophthalmologic and cardiovascular diseases, so automatic vessel segmentation may be crucial to diagnose and monitor them.
Adaptive feature recombination and recalibration for semantic segmentation: application to brain tumor segmentation in MRI
However, this is not optimal in FCN due to the spatial correspondence between units and voxels.
