P-ResUnet: Segmentation of brain tissue with Purified Residual Unet

Niu, Ke; Z, Guo; Peng, Xueping; Pei, Su

doi:10.1016/j.compbiomed.2022.106294

Cited by 21 publications

(7 citation statements)

References 32 publications

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“…Spatial weighted UNet is used for 3D CT brain images [61] with residual-inception blocks densely connected that reduces trainable parameters over the MRI dataset. A Purified and Residual UNet or P-ResUNet [62] is based on a Dilated Pyramid Block (DPB) and was used for brain tissue segmentation. This block consists of dilations of distances D = 1, 2, 3 in parallel.…”

Section: Related Workmentioning

confidence: 99%

DRD-UNet, a UNet-Like Architecture for Multi-Class Breast Cancer Semantic Segmentation

Ortega-Ruíz,

Karabağ,

Roman-Rangel

et al. 2024

IEEE Access

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Staining of histological slides with Hematoxylin and Eosin is widely used in clinical and laboratory settings as these dyes reveal nuclear structures as well as cytoplasm and collagen. For cancer diagnosis, these slides are used to recognize tissues and morphological changes. Tissue semantic segmentation is therefore important and at the same time a challenging and time-consuming task. This paper describes a UNet-like deep learning architecture called DRD-UNet, which adds a novel processing block called DRD (Dilation, Residual, and Dense block) to a UNet architecture. DRD is formed by the combination of dilated convolutions (D), residual connections (R), and dense layers (D). DRD-UNet was applied to the multi-class (tumor, stroma, inflammatory, necrosis, and other) semantic segmentation of histological images from breast cancer samples stained with Hematoxylin and Eosin. The histological images were released through the Breast Cancer Semantic Segmentation (BCSS) Challenge. DRD-UNet outperformed the original UNet architecture and 15 other UNet-based architectures on the segmentation of 12, 930 image patches extracted from regions of interest that ranged in size between 1036 × 1222 to 6813 × 7360 pixels. DRD-UNet obtained the best performance as measured with Jaccard similarity index, Dice coefficient, in a per-class comparison and accuracy for overall segmentation.

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Section: Related Workmentioning

confidence: 99%

DRD-UNet, a UNet-Like Architecture for Multi-Class Breast Cancer Semantic Segmentation

Ortega-Ruíz,

Karabağ,

Roman-Rangel

et al. 2024

IEEE Access

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“…Other models also use different forms of recurrent neural networks to predict mortality outcomes. Liu and Chen (2019) proposed a novel selective recurrent neural network with randomly connected gating units (SRCGUs), which not only reduces the number of parameters and saves time, but also dynamically adjusts their importance weights Niu et al (2022a) to select a more appropriate neural network for prediction.…”

Section: Related Workmentioning

confidence: 99%

Deep multi-modal intermediate fusion of clinical record and time series data in mortality prediction

Niu

Zhang²,

Peng³

et al. 2023

Front. Mol. Biosci.

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In intensive care units (ICUs), mortality prediction is performed by combining information from these two sources of ICU patients by monitoring patient health. Respectively, time series data generated from each patient admission to the ICU and clinical records consisting of physician diagnostic summaries. However, existing mortality prediction studies mainly cascade the multimodal features of time series data and clinical records for prediction, ignoring thecross-modal correlation between the underlying features in different modal data. To address theseissues, we propose a multimodal fusion model for mortality prediction that jointly models patients’ time-series data as well as clinical records. We apply a fine-tuned Bert model (Bio-Bert) to the patient’s clinical record to generate a holistic embedding of the text part, which is then combined with the output of an LSTM model encoding the patient’s time-series data to extract valid features. The global contextual information of each modal data is extracted using the improved fusion module to capture the correlation between different modal data. Furthermore, the improved fusion module can be easily added to the fusion features of any unimodal network and utilize existing pre-trained unimodal model weights. We use a real dataset containing 18904 ICU patients to train and evaluate our model, and the research results show that the representations obtained by themodel can achieve better prediction accuracy compared to the baseline.

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“…These algorithms and models have demonstrated outstanding performance, both in terms of the advancement of assessment metrics and the accuracy of the final predictions. Computer vision has been widely used in face recognition [16], security monitoring [17], intelligent agriculture [18], industrial product detection [19], medical image analysis [20], automatic driving [21], and other fields, saving human resources investment, improving work efficiency, and achieving greater social benefits.…”

Section: Introductionmentioning

confidence: 99%

Real-time tunnel lining leakage image semantic segmentation via multiple attention mechanisms

Tan,

Li,

Lai

et al. 2024

Meas. Sci. Technol.

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One of the key objectives in tunnel illness detection is identifying tunnel lining leakage, and deep learning-based image semantic segmentation approaches can automatically locate tunnel lining leakage. However, in order to meet the real-time processing needs of professional mobile inspection equipment, existing leakage image segmentation approaches have difficulties in identifying real-time, dealing with voids, and dealing with edge discontinuities in the leaking zone. To address the aforementioned issues, this study introduces the PP-LiteSeg-Attn model, which takes the real-time semantic segmentation model PP-LiteSeg-B as baseline model, and combines the multi-layer CBAM attention mechanism and the CoT attention mechanism. Using the publically available dataset Water-Leakage, we trained and validated the PP-LiteSeg-Attn model, and attained IoU and F1 values of 88.18% and 93.72%, respectively, outperforming similar models in both measures. Extensive experiments show that the segmentation speed of the PP-LiteSeg-Attn model reaches 112.28FPS, which meets real-time requirements, and that the model can effectively solve problems such as the appearance of voids in the seepage area, discontinuity, and fuzzy segmentation of seepage edges. The PP-LiteSeg-Attn model is better applicable to complicated tunnel settings, offering technical references for real-time diagnosis of tunnel illnesses.

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P-ResUnet: Segmentation of brain tissue with Purified Residual Unet

Cited by 21 publications

References 32 publications

DRD-UNet, a UNet-Like Architecture for Multi-Class Breast Cancer Semantic Segmentation

DRD-UNet, a UNet-Like Architecture for Multi-Class Breast Cancer Semantic Segmentation

Deep multi-modal intermediate fusion of clinical record and time series data in mortality prediction

Real-time tunnel lining leakage image semantic segmentation via multiple attention mechanisms

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