39 citations · 42 across the 5 of their papers we have counts for
7 papers
Decoding Functional Networks for Visual Categories via GNNs
Shira Karmi, Galia Avidan, Tammy Riklin Raviv
Understanding how large-scale brain networks represent visual categories is fundamental to linking perception and cortical organization. Using high-resolution 7T fMRI from the Natu…
Boundary-Aware Instance Segmentation in Microscopy Imaging
Thomas Mendelson, Joshua Francois, Galit Lahav +1
Accurate delineation of individual cells in microscopy videos is essential for studying cellular dynamics, yet separating touching or overlapping instances remains a persistent cha…
FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare
Karim Lekadir, Aasa Feragen, Abdul Joseph Fofanah +117
Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. I…
A Deep Ensemble Learning Approach to Lung CT Segmentation for COVID-19 Severity Assessment
Tal Ben-Haim, Ron Moshe Sofer, Gal Ben-Arie +2
We present a novel deep learning approach to categorical segmentation of lung CTs of COVID-19 patients. Specifically, we partition the scans into healthy lung tissues, non-lung reg…
QANet -- Quality Assurance Network for Image Segmentation
Assaf Arbelle, Eliav Elul, Tammy Riklin Raviv
We introduce a novel Deep Learning framework, which quantitatively estimates image segmentation quality without the need for human inspection or labeling. We refer to this method a…
Microscopy Cell Segmentation via Convolutional LSTM Networks
Assaf Arbelle, Tammy Riklin Raviv
Live cell microscopy sequences exhibit complex spatial structures and complicated temporal behaviour, making their analysis a challenging task. Considering cell segmentation proble…
Microscopy Cell Segmentation via Adversarial Neural Networks
Assaf Arbelle, Tammy Riklin Raviv
We present a novel method for cell segmentation in microscopy images which is inspired by the Generative Adversarial Neural Network (GAN) approach. Our framework is built on a pair…