103 research outputs found
SR: Self-supervised Spectral Regression for Hyperspectral Histopathology Image Classification
Benefited from the rich and detailed spectral information in hyperspectral
images (HSI), HSI offers great potential for a wide variety of medical
applications such as computational pathology. But, the lack of adequate
annotated data and the high spatiospectral dimensions of HSIs usually make
classification networks prone to overfit. Thus, learning a general
representation which can be transferred to the downstream tasks is imperative.
To our knowledge, no appropriate self-supervised pre-training method has been
designed for histopathology HSIs. In this paper, we introduce an efficient and
effective Self-supervised Spectral Regression (SR) method, which exploits
the low rank characteristic in the spectral domain of HSI. More concretely, we
propose to learn a set of linear coefficients that can be used to represent one
band by the remaining bands via masking out these bands. Then, the band is
restored by using the learned coefficients to reweight the remaining bands. Two
pre-text tasks are designed: (1)SR-CR, which regresses the linear
coefficients, so that the pre-trained model understands the inherent structures
of HSIs and the pathological characteristics of different morphologies;
(2)SR-BR, which regresses the missing band, making the model to learn the
holistic semantics of HSIs. Compared to prior arts i.e., contrastive learning
methods, which focuses on natural images, SR converges at least 3 times
faster, and achieves significant improvements up to 14% in accuracy when
transferring to HSI classification tasks
Gene-induced Multimodal Pre-training for Image-omic Classification
Histology analysis of the tumor micro-environment integrated with genomic
assays is the gold standard for most cancers in modern medicine. This paper
proposes a Gene-induced Multimodal Pre-training (GiMP) framework, which jointly
incorporates genomics and Whole Slide Images (WSIs) for classification tasks.
Our work aims at dealing with the main challenges of multi-modality image-omic
classification w.r.t. (1) the patient-level feature extraction difficulties
from gigapixel WSIs and tens of thousands of genes, and (2) effective fusion
considering high-order relevance modeling. Concretely, we first propose a group
multi-head self-attention gene encoder to capture global structured features in
gene expression cohorts. We design a masked patch modeling paradigm (MPM) to
capture the latent pathological characteristics of different tissues. The mask
strategy is randomly masking a fixed-length contiguous subsequence of patch
embeddings of a WSI. Finally, we combine the classification tokens of paired
modalities and propose a triplet learning module to learn high-order relevance
and discriminative patient-level information.After pre-training, a simple
fine-tuning can be adopted to obtain the classification results. Experimental
results on the TCGA dataset show the superiority of our network architectures
and our pre-training framework, achieving 99.47% in accuracy for image-omic
classification. The code is publicly available at
https://github.com/huangwudiduan/GIMP
SSDPT: Self-Supervised Dual-Path Transformer for Anomalous Sound Detection in Machine Condition Monitoring
Anomalous sound detection for machine condition monitoring has great
potential in the development of Industry 4.0. However, these anomalous sounds
of machines are usually unavailable in normal conditions. Therefore, the models
employed have to learn acoustic representations with normal sounds for
training, and detect anomalous sounds while testing. In this article, we
propose a self-supervised dual-path Transformer (SSDPT) network to detect
anomalous sounds in machine monitoring. The SSDPT network splits the acoustic
features into segments and employs several DPT blocks for time and frequency
modeling. DPT blocks use attention modules to alternately model the interactive
information about the frequency and temporal components of the segmented
acoustic features. To address the problem of lack of anomalous sound, we adopt
a self-supervised learning approach to train the network with normal sound.
Specifically, this approach randomly masks and reconstructs the acoustic
features, and jointly classifies machine identity information to improve the
performance of anomalous sound detection. We evaluated our method on the
DCASE2021 task2 dataset. The experimental results show that the SSDPT network
achieves a significant increase in the harmonic mean AUC score, in comparison
to present state-of-the-art methods of anomalous sound detection
MagicNet: Semi-Supervised Multi-Organ Segmentation via Magic-Cube Partition and Recovery
We propose a novel teacher-student model for semi-supervised multi-organ
segmentation. In teacher-student model, data augmentation is usually adopted on
unlabeled data to regularize the consistent training between teacher and
student. We start from a key perspective that fixed relative locations and
variable sizes of different organs can provide distribution information where a
multi-organ CT scan is drawn. Thus, we treat the prior anatomy as a strong tool
to guide the data augmentation and reduce the mismatch between labeled and
unlabeled images for semi-supervised learning. More specifically, we propose a
data augmentation strategy based on partition-and-recovery N cubes cross-
and within- labeled and unlabeled images. Our strategy encourages unlabeled
images to learn organ semantics in relative locations from the labeled images
(cross-branch) and enhances the learning ability for small organs
(within-branch). For within-branch, we further propose to refine the quality of
pseudo labels by blending the learned representations from small cubes to
incorporate local attributes. Our method is termed as MagicNet, since it treats
the CT volume as a magic-cube and N-cube partition-and-recovery process
matches with the rule of playing a magic-cube. Extensive experiments on two
public CT multi-organ datasets demonstrate the effectiveness of MagicNet, and
noticeably outperforms state-of-the-art semi-supervised medical image
segmentation approaches, with +7% DSC improvement on MACT dataset with 10%
labeled images. Code is available at
https://github.com/DeepMed-Lab-ECNU/MagicNet.Comment: Accepted by CVPR 202
Intriguing Findings of Frequency Selection for Image Deblurring
Blur was naturally analyzed in the frequency domain, by estimating the latent
sharp image and the blur kernel given a blurry image. Recent progress on image
deblurring always designs end-to-end architectures and aims at learning the
difference between blurry and sharp image pairs from pixel-level, which
inevitably overlooks the importance of blur kernels. This paper reveals an
intriguing phenomenon that simply applying ReLU operation on the frequency
domain of a blur image followed by inverse Fourier transform, i.e., frequency
selection, provides faithful information about the blur pattern (e.g., the blur
direction and blur level, implicitly shows the kernel pattern). Based on this
observation, we attempt to leverage kernel-level information for image
deblurring networks by inserting Fourier transform, ReLU operation, and inverse
Fourier transform to the standard ResBlock. 1x1 convolution is further added to
let the network modulate flexible thresholds for frequency selection. We term
our newly built block as Res FFT-ReLU Block, which takes advantages of both
kernel-level and pixel-level features via learning frequency-spatial
dual-domain representations. Extensive experiments are conducted to acquire a
thorough analysis on the insights of the method. Moreover, after plugging the
proposed block into NAFNet, we can achieve 33.85 dB in PSNR on GoPro dataset.
Our method noticeably improves backbone architectures without introducing many
parameters, while maintaining low computational complexity. Code is available
at https://github.com/DeepMed-Lab/DeepRFT-AAAI2023.Comment: AAAI 202
Bcl-x Pre-mRNA splicing regulates brain injury after neonatal hypoxia-ischemia
The bcl-x gene appears to play a critical role in regulating apoptosis in the developing and mature central nervous system (CNS) and following CNS injury. Two isoforms of Bcl-x are produced as a result of alternative pre-mRNA splicing: Bcl-x(L) (the long form) is anti-apoptotic, while Bcl-x(S) (short form) is pro-apoptotic. Despite the antagonistic activities of these two isoforms, little is known about how regulation of alternative splicing of bcl-x may mediate neural cell apoptosis. Here, we report that apoptotic stimuli (staurosporine or C2-ceramide) reciprocally altered Bcl-x splicing in neural cells, decreasing Bcl-x(L) while increasing Bcl-x(S). Specific knockdown of Bcl-x(S) attenuated apoptosis. In order to further define regulatory elements that influenced Bcl-x splicing, a Bcl-x minigene was constructed. Deletional analysis revealed several consensus sequences within intron 2 that altered splicing. We found that the splicing factor, CUG-binding-protein-1 (CUGBP1), bound to a consensus sequence close to the Bcl-x(L) 5′ splice site, altering the Bcl-x(L)/Bcl-x(S) ratio and influencing cell death. In vivo, neonatal hypoxia-ischemia reciprocally altered Bcl-x pre-mRNA splicing, similar to the in vitro studies. Manipulation of the splice isoforms using viral gene transfer of Bcl-x(S) shRNA into the hippocampus of rats prior to neonatal hypoxia-ischemia decreased vulnerability to injury. Moreover, alterations in nuclear CUGBP1 preceded Bcl-x splicing changes. These results suggest that alternative pre-mRNA splicing may be an important regulatory mechanism for cell death after acute neurological injury, and may potentially provide novel targets for intervention
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