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Alice M. Lucas
Alice M. Lucas
Broad Institute of MIT and Harvard
Verified email at broadinstitute.org
Title
Cited by
Cited by
Year
CellProfiler 4: improvements in speed, utility and usability
DR Stirling, MJ Swain-Bowden, AM Lucas, AE Carpenter, BA Cimini, ...
BMC bioinformatics 22, 1-11, 2021
6402021
Using deep neural networks for inverse problems in imaging: beyond analytical methods
A Lucas, M Iliadis, R Molina, AK Katsaggelos
IEEE Signal Processing Magazine 35 (1), 20-36, 2018
5712018
Generative Adversarial Networks and Perceptual Losses for Video Super-Resolution
A Lucas, S Lopez-Tapia, R Molina, AK Katsaggelos
International Conference on Image Processing, 2018
2122018
Bone mineralization and turnover in preterm infants at 8–12 years of age: the effect of early diet
MS Dr. Fewtrell, A Prentice, SC Jones, NJ Bishop, D Stirling, ...
Journal of Bone and Mineral Research 14 (5), 810-820, 1999
1621999
Open-source deep-learning software for bioimage segmentation
AM Lucas, PV Ryder, B Li, BA Cimini, KW Eliceiri, AE Carpenter
Molecular Biology of the Cell 32 (9), 823-829, 2021
602021
A single video super-resolution GAN for multiple downsampling operators based on pseudo-inverse image formation models
S López-Tapia, A Lucas, R Molina, AK Katsaggelos
Digital Signal Processing 104, 102801, 2020
182020
PyImageJ: A library for integrating ImageJ and Python
CT Rueden, MC Hiner, EL Evans III, MA Pinkert, AM Lucas, AE Carpenter, ...
Nature methods 19 (11), 1326-1327, 2022
152022
Neural networks for modeling neural spiking in S1 cortex
A Lucas, T Tomlinson, N Rohani, R Chowdhury, SA Solla, ...
Frontiers in systems neuroscience 13, 13, 2019
102019
Energy expenditure and cystic fibrosis
A Lucas, AM Prentice, RW Shepherd
The Lancet 332 (8613), 737, 1988
71988
Efficient Fine-tuning of Neural Networks for Artifact Removal in Deep Learning for Inverse Imaging Problems
A Lucas, S Lopez-Tapia, R Molina, K Aggelos K
IEEE 2019 International Conference on Image Processing (ICIP), 2019
42019
Semantic Prior Based Generative Adversarial Network for Video Super-Resolution
X Wu, A Lucas, S Lopez-Tapia, X Wang, YH Kim, R Molina, ...
27th European Signal Processing Conference (EUSIPCO), 2019
42019
Spatially Adaptive Losses for Video Super-resolution with GANs
X Wang, A Lucas, S Lopez-Tapia, X Wu, R Molina, AK Katsaggelos
IEEE 2019 International Conference on Acoustics, Speech, and Signal …, 2019
42019
Gated recurrent networks for video super resolution
S López-Tapia, A Lucas, R Molina, AK Katsaggelos
2020 28th European Signal Processing Conference (EUSIPCO), 700-704, 2021
32021
Self-supervised Fine-tuning for Correcting Super-Resolution Convolutional Neural Networks
A Lucas, S Lopez-Tapia, R Molina, AK Katsaggelos
arXiv preprint arXiv:1912.12879, 2019
32019
GAN-Based Video Super-Resolution with Direct Regularized Inversion of the Low-Resolution Formation Model
S Lopez-Tapia, A Lucas, R Molina, K Aggelos K
IEEE 2019 International Conference on Image Processing (ICIP), 2019
22019
Multiple-Degradation Video Super-Resolution with Direct Inversion of the Low-Resolution Formation Model
S Lopez-Tapia, A Lucas, R Molina, K Aggelos K
27th European Signal Processing Conference (EUSIPCO), 2019
22019
Using deep neural networks for inverse problems in imaging
A Lucas, M Iliadis, R Molina, A Katsaggelos
Signal Processing Magazine,(accepted for publication), 0
2
A General Method to Incorporate Spatial Information into Loss Functions for GAN-based Super-resolution Models
X Wang, S López-Tapia, A Lucas, X Wu, R Molina, AK Katsaggelos
arXiv preprint arXiv:2403.10589, 2024
2024
Deep Perceptual Losses and Self-supervised Fine-tuning for Image and Video Super-resolution
A Lucas
Northwestern University, 2020
2020
Self-supervised fine-tuning for image enhancement of super-resolution deep neural networks
A Lucas, S Lopez-Tapia, R Molina, AK Katsaggelos
Free radical biology & medicine., 2019
2019
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