16 research outputs found
More comprehensive facial inversion for more effective expression recognition
Facial expression recognition (FER) plays a significant role in the
ubiquitous application of computer vision. We revisit this problem with a new
perspective on whether it can acquire useful representations that improve FER
performance in the image generation process, and propose a novel generative
method based on the image inversion mechanism for the FER task, termed
Inversion FER (IFER). Particularly, we devise a novel Adversarial Style
Inversion Transformer (ASIT) towards IFER to comprehensively extract features
of generated facial images. In addition, ASIT is equipped with an image
inversion discriminator that measures the cosine similarity of semantic
features between source and generated images, constrained by a distribution
alignment loss. Finally, we introduce a feature modulation module to fuse the
structural code and latent codes from ASIT for the subsequent FER work. We
extensively evaluate ASIT on facial datasets such as FFHQ and CelebA-HQ,
showing that our approach achieves state-of-the-art facial inversion
performance. IFER also achieves competitive results in facial expression
recognition datasets such as RAF-DB, SFEW and AffectNet. The code and models
are available at https://github.com/Talented-Q/IFER-master
Smoking Cessation With 20 Hz Repetitive Transcranial Magnetic Stimulation (rTMS) Applied to Two Brain Regions: A Pilot Study
Chronic smoking impairs brain functions in the prefrontal cortex and the projecting meso-cortical limbic system. The purpose of this pilot study is to examine whether modulating the frontal brain activity using high-frequency repetitive transcranial magnetic stimulation (rTMS) can improve smoking cessation and to explore the changing pattern of the brain activity after treatment. Fourteen treatment-seeking smokers were offered a program involving 10 days of rTMS treatment with a follow-up for another 25 days. A frequency of 20 Hz rTMS was sequentially applied on the left dorso-lateral prefrontal cortex (DLPFC) and the superior medial frontal cortex (SMFC). The carbon monoxide (CO) level, withdrawal, craving scales, and neuroimaging data were collected. Ten smokers completed the entire treatment program, and 90% of them did not smoke during the 25-day follow-up time. A significant smoking craving reduction and resting brain activity reduction measured by the cerebral blood flow (CBF) and brain entropy (BEN) were observed after 10 days of 20 Hz rTMS treatments compared to the baseline. Although limited by sample size, these pilot findings definitely showed a high potential of multiple-target high-frequency rTMS in smoking cessation and the utility of fMRI for objectively assessing the treatment effects
Actively implementing an evidence-based feeding guideline for critically ill patients (NEED): a multicenter, cluster-randomized, controlled trial
Background: Previous cluster-randomized controlled trials evaluating the impact of implementing evidence-based guidelines for nutrition therapy in critical illness do not consistently demonstrate patient benefits. A large-scale, sufficiently powered study is therefore warranted to ascertain the effects of guideline implementation on patient-centered outcomes.
Methods: We conducted a multicenter, cluster-randomized, parallel-controlled trial in intensive care units (ICUs) across China. We developed an evidence-based feeding guideline. ICUs randomly allocated to the guideline group formed a local "intervention team", which actively implemented the guideline using standardized educational materials, a graphical feeding protocol, and live online education outreach meetings conducted by members of the study management committee. ICUs assigned to the control group remained unaware of the guideline content. All ICUs enrolled patients who were expected to stay in the ICU longer than seven days. The primary outcome was all-cause mortality within 28 days of enrollment.
Results: Forty-eight ICUs were randomized to the guideline group and 49 to the control group. From March 2018 to July 2019, the guideline ICUs enrolled 1399 patients, and the control ICUs enrolled 1373 patients. Implementation of the guideline resulted in significantly earlier EN initiation (1.20 vs. 1.55 mean days to initiation of EN; difference − 0.40 [95% CI − 0.71 to − 0.09]; P = 0.01) and delayed PN initiation (1.29 vs. 0.80 mean days to start of PN; difference 1.06 [95% CI 0.44 to 1.67]; P = 0.001). There was no significant difference in 28-day mortality (14.2% vs. 15.2%; difference − 1.6% [95% CI − 4.3% to 1.2%]; P = 0.42) between groups.
Conclusions: In this large-scale, multicenter trial, active implementation of an evidence-based feeding guideline reduced the time to commencement of EN and overall PN use but did not translate to a reduction in mortality from critical illness. Trial registration: ISRCTN, ISRCTN12233792. Registered November 20th, 2017
Actively implementing an evidence-based feeding guideline for critically ill patients (NEED): a multicenter, cluster-randomized, controlled trial.
BackgroundPrevious cluster-randomized controlled trials evaluating the impact of implementing evidence-based guidelines for nutrition therapy in critical illness do not consistently demonstrate patient benefits. A large-scale, sufficiently powered study is therefore warranted to ascertain the effects of guideline implementation on patient-centered outcomes.MethodsWe conducted a multicenter, cluster-randomized, parallel-controlled trial in intensive care units (ICUs) across China. We developed an evidence-based feeding guideline. ICUs randomly allocated to the guideline group formed a local "intervention team", which actively implemented the guideline using standardized educational materials, a graphical feeding protocol, and live online education outreach meetings conducted by members of the study management committee. ICUs assigned to the control group remained unaware of the guideline content. All ICUs enrolled patients who were expected to stay in the ICU longer than seven days. The primary outcome was all-cause mortality within 28 days of enrollment.ResultsForty-eight ICUs were randomized to the guideline group and 49 to the control group. From March 2018 to July 2019, the guideline ICUs enrolled 1399 patients, and the control ICUs enrolled 1373 patients. Implementation of the guideline resulted in significantly earlier EN initiation (1.20 vs. 1.55 mean days to initiation of EN; difference - 0.40 [95% CI - 0.71 to - 0.09]; P = 0.01) and delayed PN initiation (1.29 vs. 0.80 mean days to start of PN; difference 1.06 [95% CI 0.44 to 1.67]; P = 0.001). There was no significant difference in 28-day mortality (14.2% vs. 15.2%; difference - 1.6% [95% CI - 4.3% to 1.2%]; P = 0.42) between groups.ConclusionsIn this large-scale, multicenter trial, active implementation of an evidence-based feeding guideline reduced the time to commencement of EN and overall PN use but did not translate to a reduction in mortality from critical illness.Trial registrationISRCTN, ISRCTN12233792 . Registered November 20th, 2017
Actively implementing an evidence-based feeding guideline for critically ill patients (NEED): a multicenter, cluster-randomized, controlled trial (vol 26, 46, 2022)
BackgroundPrevious cluster-randomized controlled trials evaluating the impact of implementing evidence-based guidelines for nutrition therapy in critical illness do not consistently demonstrate patient benefits. A large-scale, sufficiently powered study is therefore warranted to ascertain the effects of guideline implementation on patient-centered outcomes.MethodsWe conducted a multicenter, cluster-randomized, parallel-controlled trial in intensive care units (ICUs) across China. We developed an evidence-based feeding guideline. ICUs randomly allocated to the guideline group formed a local "intervention team", which actively implemented the guideline using standardized educational materials, a graphical feeding protocol, and live online education outreach meetings conducted by members of the study management committee. ICUs assigned to the control group remained unaware of the guideline content. All ICUs enrolled patients who were expected to stay in the ICU longer than seven days. The primary outcome was all-cause mortality within 28 days of enrollment.ResultsForty-eight ICUs were randomized to the guideline group and 49 to the control group. From March 2018 to July 2019, the guideline ICUs enrolled 1399 patients, and the control ICUs enrolled 1373 patients. Implementation of the guideline resulted in significantly earlier EN initiation (1.20 vs. 1.55 mean days to initiation of EN; difference - 0.40 [95% CI - 0.71 to - 0.09]; P = 0.01) and delayed PN initiation (1.29 vs. 0.80 mean days to start of PN; difference 1.06 [95% CI 0.44 to 1.67]; P = 0.001). There was no significant difference in 28-day mortality (14.2% vs. 15.2%; difference - 1.6% [95% CI - 4.3% to 1.2%]; P = 0.42) between groups.ConclusionsIn this large-scale, multicenter trial, active implementation of an evidence-based feeding guideline reduced the time to commencement of EN and overall PN use but did not translate to a reduction in mortality from critical illness.Trial registrationISRCTN, ISRCTN12233792 . Registered November 20th, 2017
Improvements to Self-Supervised Representation Learning for Masked Image Modeling
This paper explores improvements to the masked image modeling (MIM) paradigm.
The MIM paradigm enables the model to learn the main object features of the
image by masking the input image and predicting the masked part by the unmasked
part. We found the following three main directions for MIM to be improved.
First, since both encoders and decoders contribute to representation learning,
MIM uses only encoders for downstream tasks, which ignores the impact of
decoders on representation learning. Although the MIM paradigm already employs
small decoders with asymmetric structures, we believe that continued reduction
of decoder parameters is beneficial to improve the representational learning
capability of the encoder . Second, MIM solves the image prediction task by
training the encoder and decoder together , and does not design a separate task
for the encoder . To further enhance the performance of the encoder when
performing downstream tasks, we designed the encoder for the tasks of
comparative learning and token position prediction. Third, since the input
image may contain background and other objects, and the proportion of each
object in the image varies, reconstructing the tokens related to the background
or to other objects is not meaningful for MIM to understand the main object
representations. Therefore we use ContrastiveCrop to crop the input image so
that the input image contains as much as possible only the main objects. Based
on the above three improvements to MIM, we propose a new model, Contrastive
Masked AutoEncoders (CMAE). We achieved a Top-1 accuracy of 65.84% on
tinyimagenet using the ViT-B backbone, which is +2.89 outperforming the MAE of
competing methods when all conditions are equal. Code will be made available
Medical supervised masked autoencoders: Crafting a better masking strategy and efficient fine-tuning schedule for medical image classification
Masked autoencoders (MAEs) have displayed significant potential in the
classification and semantic segmentation of medical images in the last year.
Due to the high similarity of human tissues, even slight changes in medical
images may represent diseased tissues, necessitating fine-grained inspection to
pinpoint diseased tissues. The random masking strategy of MAEs is likely to
result in areas of lesions being overlooked by the model. At the same time,
inconsistencies between the pre-training and fine-tuning phases impede the
performance and efficiency of MAE in medical image classification. To address
these issues, we propose a medical supervised masked autoencoder (MSMAE) in
this paper. In the pre-training phase, MSMAE precisely masks medical images via
the attention maps obtained from supervised training, contributing to the
representation learning of human tissue in the lesion area. During the
fine-tuning phase, MSMAE is also driven by attention to the accurate masking of
medical images. This improves the computational efficiency of the MSMAE while
increasing the difficulty of fine-tuning, which indirectly improves the quality
of MSMAE medical diagnosis. Extensive experiments demonstrate that MSMAE
achieves state-of-the-art performance in case with three official medical
datasets for various diseases. Meanwhile, transfer learning for MSMAE also
demonstrates the great potential of our approach for medical semantic
segmentation tasks. Moreover, the MSMAE accelerates the inference time in the
fine-tuning phase by 11.2% and reduces the number of floating-point operations
(FLOPs) by 74.08% compared to a traditional MAE