304 citations · 838 across the 11 of their papers we have counts for
12 papers
OCELOT: Overlapped Cell on Tissue Dataset for Histopathology
Jeongun Ryu, Aaron Valero Puche, JaeWoong Shin +9
Cell detection is a fundamental task in computational pathology that can be used for extracting high-level medical information from whole-slide images. For accurate cell detection,…
Benchmarking Self-Supervised Learning on Diverse Pathology Datasets
Mingu Kang, Heon Song, Seonwook Park +2
Computational pathology can lead to saving human lives, but models are annotation hungry and pathology images are notoriously expensive to annotate. Self-supervised learning has sh…
Variability Matters : Evaluating inter-rater variability in histopathology for robust cell detection
Cholmin Kang, Chunggi Lee, Heon Song +2
Large annotated datasets have been a key component in the success of deep learning. However, annotating medical images is challenging as it requires expertise and a large budget. I…
Interactive Multi-Class Tiny-Object Detection
Chunggi Lee, Seonwook Park, Heon Song +5
Annotating tens or hundreds of tiny objects in a given image is laborious yet crucial for a multitude of Computer Vision tasks. Such imagery typically contains objects from various…
Multi-stage Deep Layer Aggregation for Brain Tumor Segmentation
Carlos A. Silva, Adriano Pinto, Sérgio Pereira +1
Gliomas are among the most aggressive and deadly brain tumors. This paper details the proposed Deep Neural Network architecture for brain tumor segmentation from Magnetic Resonance…
Combining unsupervised and supervised learning for predicting the final stroke lesion
Adriano Pinto, Sérgio Pereira, Raphael Meier +4
Predicting the final ischaemic stroke lesion provides crucial information regarding the volume of salvageable hypoperfused tissue, which helps physicians in the difficult decision-…
Detecting Infrared Single Photons with Near-Unity System Detection Efficiency
Jin Chang, Johannes W. N. Los, Jaime Oscar Tenorio-Pearl +9
Single photon detectors are indispensable tools in optics, from fundamental measurements to quantum information processing. The ability of superconducting nanowire single photon de…
Adaptive feature recombination and recalibration for semantic segmentation with Fully Convolutional Networks
Sergio Pereira, Adriano Pinto, Joana Amorim +3
Fully Convolutional Networks have been achieving remarkable results in image semantic segmentation, while being efficient. Such efficiency results from the capability of segmenting…
Retinal vessel segmentation based on Fully Convolutional Neural Networks
Américo Oliveira, Sérgio Pereira, Carlos A. Silva
The retinal vascular condition is a reliable biomarker of several ophthalmologic and cardiovascular diseases, so automatic vessel segmentation may be crucial to diagnose and monito…
Automatic brain tumor grading from MRI data using convolutional neural networks and quality assessment
Sergio Pereira, Raphael Meier, Victor Alves +2
Glioblastoma Multiforme is a high grade, very aggressive, brain tumor, with patients having a poor prognosis. Lower grade gliomas are less aggressive, but they can evolve into high…
Enhancing clinical MRI Perfusion maps with data-driven maps of complementary nature for lesion outcome prediction
Adriano Pinto, Sergio Pereira, Raphael Meier +4
Stroke is the second most common cause of death in developed countries, where rapid clinical intervention can have a major impact on a patient's life. To perform the revascularizat…
Adaptive feature recombination and recalibration for semantic segmentation: application to brain tumor segmentation in MRI
Sérgio Pereira, Victor Alves, Carlos A. Silva
Convolutional neural networks (CNNs) have been successfully used for brain tumor segmentation, specifically, fully convolutional networks (FCNs). FCNs can segment a set of voxels a…