A new ore image segmentation method based on Swin-Unet

Tang, Xiangxiang; Wang, Xiaoli; Yan, Ning; Fu, Shichao; Xiong, Wei; Liao, Qian

doi:10.1109/cac57257.2022.10055952

Cited by 5 publications

(1 citation statement)

References 15 publications

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“…Where a standard UNet uses convolutions at different scales for hierarchical feature extraction, Swin-Unet uses Swin Transformers [ 11 ] at different scales to accomplish the same. Because transformers and self-attention overcome the inherent locality of Convolutional Neural Networks (CNNs), Swin-Unet has been shown to outperform the standard convolutional based UNet architecture in publicly-available semantic segmentation benchmarks [ 9 ], as well as in private datasets [ 12 , 13 ], making it the state-of-the-art in medical semantic segmentation.…”

Section: Introductionmentioning

confidence: 99%

Visual Prompting based Incremental Learning forSemantic Segmentation of MultiplexImmuno-Flourescence Microscopy Imagery

Faulkenberry,

Prasad,

Maric

et al. 2023

Preprint

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Deep learning approaches are state-of-the-art for semantic segmentation of medical images, but unlike many deep learning applications, medical segmentation is characterized by small amounts of annotated training data. Thus, while mainstream deep learning approaches focus on performance in domains with large training sets, researchers in the medical imaging field must apply new methods in creative ways to meet the more constrained requirements of medical datasets. We propose a framework for incrementally fine-tuning a multi-class segmentation of a high-resolution multiplex (multi-channel) immuno-flourescence image of a rat brain section, using a minimal amount of labelling from a human expert. Our framework begins with a modified Swin-UNet architecture that treats each biomarker in the multiplex image separately and learns an initial ''global'' segmentation (\emph{pre-training}). This is followed by incremental learning and refinement of each class using a very limited amount of additional labeled data provided by a human expert for each region and its surroundings. This incremental learning utilizes the multi-class weights as an initialization and uses the additional labels to steer the network and optimize it for each region in the image. In this way, an expert can identify errors in the multi-class segmentation and rapidly correct them by supplying the model with additional annotations hand-picked from the region. In addition to increasing the speed of annotation and reducing the amount of labelling, we show that our proposed method outperforms a traditional multi-class segmentation by a large margin.

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Section: Introductionmentioning

confidence: 99%

Visual Prompting based Incremental Learning forSemantic Segmentation of MultiplexImmuno-Flourescence Microscopy Imagery

Faulkenberry,

Prasad,

Maric

et al. 2023

Preprint

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Visual Prompting Based Incremental Learning for Semantic Segmentation of Multiplex Immuno-Flourescence Microscopy Imagery

Faulkenberry,

Prasad,

Maric

et al. 2024

Neuroinform

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Research on anti-clogging of ore conveyor belt with static image based on improved Fast-SCNN and U-Net

Liu,

Zhang,

Xiao

2023

Sci Rep

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This paper presents an improved Fast-Segmentation Convolutional Neural Network (Fast-SCNN) and U-Net networks based on the channel attention mechanism. While ensuring the speed of network detection, the accuracy of image segmentation is also considered. The experimental results show that the accuracy rate of improved Fast-SCNN based on the channel attention mechanism is greatly improved compared with the original Fast-SCNN, reaching 88.056%, and the mean intersection over union is also improved to a certain extent, reaching 81.087%, and the detection speed is better than the original Fast-SCNN network. The accuracy of improved U-Net network based on the channel attention mechanism is 0.91805, which is better than the original U-Net network. In terms of detection speed, the improved U-Net network based on channel attention mechanism has greatly improved compared with the original U-Net network, reaching 24.02 frames per second. In addition, a method of preventing clogging of ore conveyor belts based on static image detection is proposed in this paper. By judging and predicting the blockage of the ore conveyor belt. When the conveyor belt is about to be blocked or has been blocked, the fuzzy algorithm is used to control the ore conveyor belt to slow down and stop, to improve the safety and efficiency of the conveyor belt.

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A new ore image segmentation method based on Swin-Unet

Cited by 5 publications

References 15 publications

Visual Prompting based Incremental Learning forSemantic Segmentation of MultiplexImmuno-Flourescence Microscopy Imagery

Visual Prompting based Incremental Learning forSemantic Segmentation of MultiplexImmuno-Flourescence Microscopy Imagery

Visual Prompting Based Incremental Learning for Semantic Segmentation of Multiplex Immuno-Flourescence Microscopy Imagery

Research on anti-clogging of ore conveyor belt with static image based on improved Fast-SCNN and U-Net

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