A5069564619- Neural dynamics and brain function
- Medical Imaging Techniques and Applications
- Neuroscience and Neuropharmacology Research
- Radiomics and Machine Learning in Medical Imaging
- Neurobiology and Insect Physiology Research
- Advanced Chemical Sensor Technologies
- Metabolomics and Mass Spectrometry Studies
- Biochemical Analysis and Sensing Techniques
- Face Recognition and Perception
- Olfactory and Sensory Function Studies
- Advanced MRI Techniques and Applications
- Visual Attention and Saliency Detection
- Digital Imaging for Blood Diseases
- Radiation Detection and Scintillator Technologies
- Advanced Memory and Neural Computing
- Medical Image Segmentation Techniques
- Nephrotoxicity and Medicinal Plants
- AI in cancer detection
- Drug-Induced Hepatotoxicity and Protection
Stanford University
2023-2024
Yale University
2022
Columbia University
2019-2020
This paper describes the setup of a segmentation competition for automatic extraction Multiple Sclerosis (MS) lesions from brain Magnetic Resonance Imaging (MRI) data. is one three competitions that make up comparison workshop at 2008 Medical Image Computing and Computer Assisted Intervention (MICCAI) conference was modeled after successful on liver caudate 2007 MICCAI conference. In this paper, rationale organizing discussed, training test data sets both tasks are described scoring system...
Recent work suggests goal-driven training of neural networks can be used to model activity in the brain. While response properties neurons artificial bear similarities those brain, network architectures are often constrained different. Here we ask if a recover both representations and, architecture is unconstrained and optimized, anatomical circuits. We demonstrate this system where connectivity functional organization have been characterized, namely, head direction circuits rodent fruit...
Scene perception involves extracting the identities of objects comprising a scene in conjunction with their configuration (the spatial layout scene). How object identity and information is weighted during processing how this weighting evolves over course however, not fully understood. Recent developments convolutional neural networks (CNNs) have demonstrated aptitude at tasks identified correlations between CNNs human brain. Here we examined four CNN architectures (Alexnet, Resnet18,...
Normalization correction is a critical step in image reconstruction for correcting detector efficiency variations and reducing related artifacts reconstructed positron emission tomography (PET) images. One common approach direct normalization, which requires large number of counts per line response from known normalization phantom collected within reasonable time frame. However, this method can be extremely time-consuming due to using typically relatively low-activity uniform source...