Using a Convolutional Siamese Network for Image-Based Plant Species Identification with Small Datasets

Background：Lymph node metastasis of lung cancer plays an important part in lung cancer diagnosis since its close relationship with patients’ prognosis and subsequent treatment, however, diagnosis of lymph node metastases often was tedious and takes too much time for pathologists. Automatized classification with few-shot learning algorithms is required for clinical situations that only unbalanced and small datasets can be collected. We developed a few-shot learning algorithms for automatized classification of lymphatic metastasis of lung carcinoma from Whole Slide Images (WSIs) through a deep convolutional neural network (DCNN).Material and methods: Lymph node slides of patients with lung cancer collected from July to December 2018 were reviewed and used for model development retrospectively. A total of 1701 lymph node slides from 453 lung cancer patients with different histological types were enrolled. Each slide could provide a different number of patches, in which 6, 8, 10, 20, and 30 positive patches and 12, 16, 20, 40, and 60 negative patches for training and validation, respectively. A total of 1577 WSIs were used. The receiver operating characteristic (ROC) analysis of the model was performed for estimating the performance and compared to the traditional deep learning approach Resnet34.Results: Deep learning methods, when trained with very few shots (6 positive/12 negative, 8 positive/16 negative, and 10 positive/20 negative patches), the traditional deep learning approach Resnet34 was outperformed by our Aitrox model. When the dataset was raised to 20 positive/40 negative patches, similar performance was achieved with AUC 0.5895 (95% CI, 0.5833-0.5910) for Resnet34 and 0.6124 (95% CI, 0.6077-0.6147) for our Aitrox model. Resnet34 over-performed our Aitrox model (AUC=0.6450 [95%CI, 0.6397-0.6470]) with an AUC of 0.7481 (95%CI, 0.7120-0.7485) when the training and validation dataset was raised to 30 positive/60 negative patches.Conclusions: This proposed few-shot method may address AI approaches to the diagnosis of lymphatic metastasis of lung carcinoma and other kinds of carcinoma in the future.

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“…We chose Euclidean distance [22][23][24] as our distance metric. Take one of the small datasets for example, the number .…”

Section: Design Choicementioning

confidence: 99%

WITHDRAWN: Few-shot Learning on the Diagnosis of Lymphatic Metastasis of Lung Carcinoma

Teng

Zhang

Wei

et al. 2021

Preprint

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“…latest advances presented e.g. in Figueroa‐Mata and Mata‐Montero 2020, Villon et al 2020), or by more strongly considering additional information e.g. of geographical locations as suggested for instance by Wittich et al (2018) or using other ancillary information (Goldsmith et al 2016, Terry et al 2020), e.g.…”

Section: Discussionmentioning

confidence: 99%

Crowd‐sourced plant occurrence data provide a reliable description of macroecological gradients

Mahecha

Rzanny

Kraemer³

et al. 2021

Ecography

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Deep learning algorithms classify plant species with high accuracy, and smartphone applications leverage this technology to enable users to identify plant species in the field. The question we address here is whether such crowd‐sourced data contain substantial macroecological information. In particular, we aim to understand if we can detect known environmental gradients shaping plant co‐occurrences. In this study we analysed 1 million data points collected through the use of the mobile app Flora Incognita between 2018 and 2019 in Germany and compared them with Florkart, containing plant occurrence data collected by more than 5000 floristic experts over a 70‐year period. The direct comparison of the two data sets reveals that the crowd‐sourced data particularly undersample areas of low population density. However, using nonlinear dimensionality reduction we were able to uncover macroecological patterns in both data sets that correspond well to each other. Mean annual temperature, temperature seasonality and wind dynamics as well as soil water content and soil texture represent the most important gradients shaping species composition in both data collections. Our analysis describes one way of how automated species identification could soon enable near real‐time monitoring of macroecological patterns and their changes, but also discusses biases that must be carefully considered before crowd‐sourced biodiversity data can effectively guide conservation measures.

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“…For comparison, FSL techniques based on metric-learning were also implemented. A convolutional Siamese neural network was developed to find the relationship between two comparable classes [37]. Recently, researchers have reported that Siamese networks perform well in complicated FSL tasks with shared weights of the backbone CNN model [16].…”

Section: Other Types Of Few-shot Learningmentioning

confidence: 99%

Feasibility study to improve deep learning in OCT diagnosis of rare retinal diseases with few-shot classification

Yoo

Choi

Kim

2021

Med Biol Eng Comput

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Deep learning (DL) has been successfully applied to the diagnosis of ophthalmic diseases. However, rare diseases are commonly neglected due to insufficient data. Here, we demonstrate that few-shot learning (FSL) using a generative adversarial network (GAN) can improve the applicability of DL in the optical coherence tomography (OCT) diagnosis of rare diseases. Four major classes with a large number of datasets and five rare disease classes with a few-shot dataset are included in this study. Before training the classifier, we constructed GAN models to generate pathological OCT images of each rare disease from normal OCT images. The Inception-v3 architecture was trained using an augmented training dataset, and the final model was validated using an independent test dataset. The synthetic images helped in the extraction of the characteristic features of each rare disease. The proposed DL model demonstrated a significant improvement in the accuracy of the OCT diagnosis of rare retinal diseases and outperformed the traditional DL models, Siamese network, and prototypical network. By increasing the accuracy of diagnosing rare retinal diseases through FSL, clinicians can avoid neglecting rare diseases with DL assistance, thereby reducing diagnosis delay and patient burden.

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Using a Convolutional Siamese Network for Image-Based Plant Species Identification with Small Datasets

Cited by 31 publications

References 23 publications

WITHDRAWN: Few-shot Learning on the Diagnosis of Lymphatic Metastasis of Lung Carcinoma

WITHDRAWN: Few-shot Learning on the Diagnosis of Lymphatic Metastasis of Lung Carcinoma

Crowd‐sourced plant occurrence data provide a reliable description of macroecological gradients

Feasibility study to improve deep learning in OCT diagnosis of rare retinal diseases with few-shot classification

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