Evaluation of the Classification Accuracy of the Kidney Biopsy Direct Immunofluorescence through Convolutional Neural Networks

Ligabue, Giulia; Pollastri, Federico; Fontana, Francesco; Leonelli, Marco; Furci, Luciana; Giovanella, Silvia; Alfano, Gaetano; Cappelli, Gianni; Testa, Francesca; Bolelli, Federico; Grana, Costantino; Magistroni, Riccardo

doi:10.2215/cjn.03210320

Cited by 38 publications

(25 citation statements)

References 29 publications

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“…Among them, deep learning algorithms have revealed to be the most effective solutions. As a matter of fact, Convolutional Neural Networks (CNNs) are currently the cornerstone of medical image analysis [20], [21], [22], [23], [24].…”

Section: Introductionmentioning

confidence: 99%

Supporting Skin Lesion Diagnosis with Content-Based Image Retrieval

Allegretti

Bolelli

Pollastri

et al. 2021

2020 25th International Conference on Pattern Recognition (ICPR)

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In recent years, many attempts have been dedicated to the creation of automated devices that could assist both expert and beginner dermatologists towards fast and early diagnosis of skin lesions. Tasks such as skin lesion classification and segmentation have been extensively addressed with deep learning algorithms, which in some cases reach a diagnostic accuracy comparable to that of expert physicians. However, the general lack of interpretability and reliability severely hinders the ability of those approaches to actually support dermatologists in the diagnosis process.In this paper a novel skin image retrieval system is presented, which exploits features extracted by Convolutional Neural Networks to gather similar images from a publicly available dataset, in order to assist the diagnosis process of both expert and novice practitioners. In the proposed framework, ResNet-50 is initially trained for the classification of dermoscopic images; then, the feature extraction part is isolated, and an embedding network is built on top of it. The embedding learns an alternative representation, which allows to check image similarity by means of a distance measure.Experimental results reveal that the proposed method is able to select meaningful images, which can effectively boost the classification accuracy of human dermatologists.

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Section: Introductionmentioning

confidence: 99%

Supporting Skin Lesion Diagnosis with Content-Based Image Retrieval

Allegretti

Bolelli

Pollastri

et al. 2021

2020 25th International Conference on Pattern Recognition (ICPR)

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“…Finally, Ligabue et al [ 23 ] evaluated the role of a CNN as a support tool for kidney immunofluorescence reporting and found that CNNs were 117 times faster than human inspectors in analyzing 180 test images. The accuracy of the CNN was comparable with that of experienced pathologists in the field.…”

Section: Application Of Ai In Kidney Transplantationmentioning

confidence: 99%

Artificial intelligence and kidney transplantation

Seyahi¹,

Özcan²

2021

WJT

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Artificial intelligence and its primary subfield, machine learning, have started to gain widespread use in medicine, including the field of kidney transplantation. We made a review of the literature that used artificial intelligence techniques in kidney transplantation. We located six main areas of kidney transplantation that artificial intelligence studies are focused on: Radiological evaluation of the allograft, pathological evaluation including molecular evaluation of the tissue, prediction of graft survival, optimizing the dose of immunosuppression, diagnosis of rejection, and prediction of early graft function. Machine learning techniques provide increased automation leading to faster evaluation and standardization, and show better performance compared to traditional statistical analysis. Artificial intelligence leads to improved computer-aided diagnostics and quantifiable personalized predictions that will improve personalized patient care.

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“…Deep learning methods have been employed in several medical fields, such as renal biopsy [25], image retrieval [26], and the detection of multiple forms of cancer [27].…”

Section: Related Workmentioning

confidence: 99%

A deep analysis on high‐resolution dermoscopic image classification

et al. 2021

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Convolutional neural networks (CNNs) have been broadly employed in dermoscopic image analysis, mainly as a result of the large amount of data gathered by the International Skin Imaging Collaboration (ISIC). As in many other medical imaging domains, state-ofthe-art methods take advantage of architectures developed for other tasks, frequently assuming full transferability between enormous sets of natural images (e.g. ImageNet) and dermoscopic images, which is not always the case. A comprehensive analysis on the effectiveness of state-of-the-art deep learning techniques when applied to dermoscopic image analysis is provided. To achieve this goal, the authors consider several CNNs architectures and analyse how their performance is affected by the size of the network, image resolution, data augmentation process, amount of available data, and model calibration. Moreover, taking advantage of the analysis performed, a novel ensemble method to further increase the classification accuracy is designed. The proposed solution achieved the third best result in the 2019 official ISIC challenge, with an accuracy of 0.593.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Evaluation of the Classification Accuracy of the Kidney Biopsy Direct Immunofluorescence through Convolutional Neural Networks

Cited by 38 publications

References 29 publications

Supporting Skin Lesion Diagnosis with Content-Based Image Retrieval

Supporting Skin Lesion Diagnosis with Content-Based Image Retrieval

Artificial intelligence and kidney transplantation

A deep analysis on high‐resolution dermoscopic image classification

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