Eric Granger
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Explore the profile of Eric Granger including associated specialties, affiliations and a list of published articles.
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7
Citations
87
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Recent Articles
1.
Khajehpiri B, Granger E, de Zambotti M, Baker F, Yuksel D, Forouzanfar M
IEEE Sens J
. 2025 Feb;
24(12):19590-19600.
PMID: 39959563
Nocturnal blood pressure (BP) monitoring offers valuable insights into various aspects of human wellbeing, particularly cardiovascular health. Despite recent advancements in medical technology, there remains a pressing need for a...
2.
Shamsolmoali P, Zareapoor M, Das S, Granger E, Garcia S
IEEE Trans Neural Netw Learn Syst
. 2024 Jan;
36(2):2480-2494.
PMID: 38265908
Capsule networks (CapsNets) aim to parse images into a hierarchy of objects, parts, and their relationships using a two-step process involving part-whole transformation and hierarchical component routing. However, this hierarchical...
3.
Shamsolmoali P, Zareapoor M, Das S, Garcia S, Granger E, Yang J
IEEE Trans Cybern
. 2022 May;
53(2):874-886.
PMID: 35522633
Image-to-image (I2I) translation has become a key asset for generative adversarial networks. Convolutional neural networks (CNNs), despite having a significant performance, are not able to capture the spatial relationships among...
4.
Belharbi S, Rony J, Dolz J, Ben Ayed I, McCaffrey L, Granger E
IEEE Trans Med Imaging
. 2021 Oct;
41(3):702-714.
PMID: 34705638
Weakly-supervised learning (WSL) has recently triggered substantial interest as it mitigates the lack of pixel-wise annotations. Given global image labels, WSL methods yield pixel-level predictions (segmentations), which enable to interpret...
5.
Kervadec H, Bouchtiba J, Desrosiers C, Granger E, Dolz J, Ben Ayed I
Med Image Anal
. 2020 Oct;
67:101851.
PMID: 33080507
Widely used loss functions for CNN segmentation, e.g., Dice or cross-entropy, are based on integrals over the segmentation regions. Unfortunately, for highly unbalanced segmentations, such regional summations have values that...
6.
Kervadec H, Dolz J, Tang M, Granger E, Boykov Y, Ben Ayed I
Med Image Anal
. 2019 Mar;
54:88-99.
PMID: 30851541
Weakly-supervised learning based on, e.g., partially labelled images or image-tags, is currently attracting significant attention in CNN segmentation as it can mitigate the need for full and laborious pixel/voxel annotations....
7.
Dolz J, Xu X, Rony J, Yuan J, Liu Y, Granger E, et al.
Med Phys
. 2018 Oct;
45(12):5482-5493.
PMID: 30328624
Purpose: Precise segmentation of bladder walls and tumor regions is an essential step toward noninvasive identification of tumor stage and grade, which is critical for treatment decision and prognosis of...
8.
Carbonneau M, Granger E, Gagnon G
IEEE Trans Neural Netw Learn Syst
. 2018 Oct;
30(5):1441-1451.
PMID: 30281492
A growing number of applications, e.g., video surveillance and medical image analysis, require training recognition systems from large amounts of weakly annotated data, while some targeted interactions with a domain...