Search Results for author:
Found 7 papers, 4 papers with code
Screener: Self-supervised Pathology Segmentation Model for 3D Medical Images
Accurate segmentation of all pathological findings in 3D medical images remains a significant challenge, as supervised models are limited to detecting only the few pathology classes annotated in existing datasets.
Medical Semantic Segmentation with Diffusion Pretrain
We introduce an auxiliary diffusion process to pretrain a model that produce generalizable feature representations, useful for a variety of downstream segmentation tasks.
Cirbo: A New Tool for Boolean Circuit Analysis and Synthesis
We present an open-source tool for manipulating Boolean circuits.
Anatomical Positional Embeddings
We train our APE model on 8400 publicly available CT images of abdomen and chest regions.
vox2vec: A Framework for Self-supervised Contrastive Learning of Voxel-level Representations in Medical Images
This paper introduces vox2vec - a contrastive method for self-supervised learning (SSL) of voxel-level representations.
CT-based COVID-19 Triage: Deep Multitask Learning Improves Joint Identification and Severity Quantification
We train our model on approximately 2000 publicly available CT studies and test it with a carefully designed set consisting of 32 COVID-19 studies, 30 cases with bacterial pneumonia, 31 healthy patients, and 30 patients with other lung pathologies to emulate a typical patient flow in an out-patient hospital.
Incorporating Task-Specific Structural Knowledge into CNNs for Brain Midline Shift Detection
Midline shift (MLS) is a well-established factor used for outcome prediction in traumatic brain injury, stroke and brain tumors.
