A Multidimensional Choledoch Database and Benchmarks for Cholangiocarcinoma Diagnosis

Zhang, Qing; Li, Qingli; Yu, Guanzhen; Li, Sun; Zhou, Mei; Chu, Junhao

doi:10.1109/access.2019.2947470

Cited by 55 publications

(20 citation statements)

References 45 publications

(49 reference statements)

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“…As is shown in Figure 12, the light is assigned to gray scientific complementary metal oxide semiconductor (sCMOS) and color charge coupled device detector (CCD) to acquire both hyperspectral and color image simultaneously. More detail about this system can be found in our previous works [3]. The experiments are run on four CPU cores (the Inter(R) Xeon(R) CPU E3-1225V2 @ 3.20 GHz).…”

Section: Resultsmentioning

confidence: 99%

“…With the development of artificial intelligence in recent years, pathological image analysis has also gradually moved from qualitative to quantitative. Whole slide pathological databases are an indispensable step for quantitative research because region images are unable to pay attention to pathological materials such as thick smears, obscuring material and overlapping cells, and so on [1][2][3]. There have been several researchers studying whole slide imaging (WSI) systems and algorithms, such as instant focal plane detection based WSI system [4], automated blur detection based WSI system [5] and deep learning based WSI system [6].…”

Section: Introductionmentioning

confidence: 99%

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An autofocus algorithm considering wavelength changes for large scale microscopic hyperspectral pathological imaging system

Zhang

Wang

et al. 2022

Journal of Biophotonics

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Microscopic hyperspectral imaging technology has been widely used to acquire pathological information of tissue sections.Autofocus is one of the most important steps in microscopic hyperspectral imaging systems to capture large scale or even whole slide images of pathological slides with high quality and high speed. However, there are quite few autofocus algorithm put forward for the microscopic hyperspectral imaging system. Therefore, this article proposes a Laplace operator based autofocus algorithm for microscopic hyperspectral imaging system which takes the influence of wavelength changes into consideration. Through the proposed algorithm, the focal length for each wavelength can be adjusted automatically to ensure that each single band image can be autofocused precisely with adaptive image sharpness evaluation method. In addition, to increase the capture speed, the relationship of wavelength and focal length is derived and the focal offsets among different single band images are calculated for pre-focusing. We have employed the proposed method on our own datasets and the experimental results show that it can capture large-scale microscopic hyperspectral pathology images with high precise.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An autofocus algorithm considering wavelength changes for large scale microscopic hyperspectral pathological imaging system

Zhang

Wang

et al. 2022

Journal of Biophotonics

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“…Extensive experimentation on the hyperspectral pathology dataset, Cholangiocarcinoma [147], shows the effectiveness of SpecTr as also shown in Fig. 7.…”

Section: D Medical Segmentationmentioning

confidence: 89%

Transformers in Medical Imaging: A Survey

Shamshad¹,

Khan²,

Zamir³

et al. 2022

Preprint

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Following unprecedented success on the natural language tasks, Transformers have been successfully applied to several computer vision problems, achieving state-of-the-art results and prompting researchers to reconsider the supremacy of convolutional neural networks (CNNs) as de facto operators. Capitalizing on these advances in computer vision, the medical imaging field has also witnessed growing interest for Transformers that can capture global context compared to CNNs with local receptive fields. Inspired from this transition, in this survey, we attempt to provide a comprehensive review of the applications of Transformers in medical imaging covering various aspects, ranging from recently proposed architectural designs to unsolved issues. Specifically, we survey the use of Transformers in medical image segmentation, detection, classification, reconstruction, synthesis, registration, clinical report generation, and other tasks. In particular, for each of these applications, we develop taxonomy, identify application-specific challenges as well as provide insights to solve them, and highlight recent trends. Further, we provide a critical discussion of the field's current state as a whole, including the identification of key challenges, open problems, and outlining promising future directions. We hope this survey will ignite further interest in the community and provide researchers with an up-to-date reference regarding applications of Transformer models in medical imaging. Finally, to cope with the rapid development in this field, we intend to regularly update the relevant latest papers and their open-source implementations at https://github.com/fahadshamshad/awesome-transformers-in-medical-imaging.

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“…Dataset: We use multi-dimensional choledoch dataset for cholangiocarcinoma diagnosis [17], where 514 scenes with high quality labels are selected for training and testing. When using microscopy hyperspectral imaging system to capture images, the light transmitted from the choledoch tissue slice was collected by the microscope with the objective lens of 20×.…”

Section: Methodsmentioning

confidence: 99%

SpecTr: Spectral Transformer for Hyperspectral Pathology Image Segmentation

Yun,

Wang,

Chen

et al. 2021

Preprint

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Hyperspectral imaging (HSI) unlocks the huge potential to a wide variety of applications relied on high-precision pathology image segmentation, such as computational pathology and precision medicine. Since hyperspectral pathology images benefit from the rich and detailed spectral information even beyond the visible spectrum, the key to achieve high-precision hyperspectral pathology image segmentation is to felicitously model the context along high-dimensional spectral bands. Inspired by the strong context modeling ability of transformers, we hereby, for the first time, formulate the contextual feature learning across spectral bands for hyperspectral pathology image segmentation as a sequence-tosequence prediction procedure by transformers. To assist spectral context learning procedure, we introduce two important strategies: (1) a sparsity scheme enforces the learned contextual relationship to be sparse, so as to eliminates the distraction from the redundant bands; (2) a spectral normalization, a separate group normalization for each spectral band, mitigates the nuisance caused by heterogeneous underlying distributions of bands. We name our method Spectral Transformer (SpecTr), which enjoys two benefits: (1) it has a strong ability to model long-range dependency among spectral bands, and (2) it jointly explores the spatialspectral features of HSI. Experiments show that SpecTr outperforms other competing methods in a hyperspectral pathology image segmentation benchmark without the need of pre-training. Code is available at https://github.com/hfut-xc-yun/SpecTr.

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A Multidimensional Choledoch Database and Benchmarks for Cholangiocarcinoma Diagnosis

Cited by 55 publications

References 45 publications

An autofocus algorithm considering wavelength changes for large scale microscopic hyperspectral pathological imaging system

An autofocus algorithm considering wavelength changes for large scale microscopic hyperspectral pathological imaging system

Transformers in Medical Imaging: A Survey

SpecTr: Spectral Transformer for Hyperspectral Pathology Image Segmentation

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