PFA-ScanNet: Pyramidal Feature Aggregation with Synergistic Learning for Breast Cancer Metastasis Analysis

Zhao, Zixu; Lin, Huangjing; Chen, Hao; Heng, Pheng‐Ann

doi:10.1007/978-3-030-32239-7_65

Cited by 19 publications

(15 citation statements)

References 14 publications

(28 reference statements)

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“…In addition, most of the methods took hundreds of minutes to run, which created a barrier to clinical application. To improve computational efficiency, Kong et al (111) and Zhao et al (112) used transfer learning to accelerate model convergence, reducing the time for a single WSI review to 5.6 and 7.2 min. Afterwards, Campanella et al (113) trained a weakly supervised learning model based on 44,732 full-slice scanned images, avoiding the manual process of extensive annotation, and obtained an AUC value of 0.965 in a test of identifying axillary lymph node metastases in breast cancer.…”

Section: Organized the Cancer Metastases In Lymphmentioning

confidence: 99%

Progress on deep learning in digital pathology of breast cancer: a narrative review

Zhu¹,

Liu²,

Li³

2022

Gland Surg

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Background and Objective: Pathology is the gold standard criteria for breast cancer diagnosis and has important guiding value in formulating the clinical treatment plan and predicting the prognosis.However, traditional microscopic examinations of tissue sections are time consuming and labor intensive, with unavoidable subjective variations. Deep learning (DL) can evaluate and extract the most important information from images with less need for human instruction, providing a promising approach to assist in the pathological diagnosis of breast cancer. To provide an informative and up-to-date summary on the topic of DL-based diagnostic systems for breast cancer pathology image analysis and discuss the advantages and challenges to the routine clinical application of digital pathology.Methods: A PubMed search with keywords ("breast neoplasm" or "breast cancer") and ("pathology" or "histopathology") and ("artificial intelligence" or "deep learning") was conducted. Relevant publications in English published from January 2000 to October 2021 were screened manually for their title, abstract, and even full text to determine their true relevance. References from the searched articles and other supplementary articles were also studied. Key Content and Findings: DL-based computerized image analysis has obtained impressive achievementsin breast cancer pathology diagnosis, classification, grading, staging, and prognostic prediction, providing powerful methods for faster, more reproducible, and more precise diagnoses. However, all artificial intelligence (AI)-assisted pathology diagnostic models are still in the experimental stage. Improving their economic efficiency and clinical adaptability are still required to be developed as the focus of further researches.Conclusions: Having searched PubMed and other databases and summarized the application of DL-based AI models in breast cancer pathology, we conclude that DL is undoubtedly a promising tool for assisting pathologists in routines, but further studies are needed to realize the digitization and automation of clinical pathology.

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Section: Organized the Cancer Metastases In Lymphmentioning

confidence: 99%

Progress on deep learning in digital pathology of breast cancer: a narrative review

Zhu¹,

Liu²,

Li³

2022

Gland Surg

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“…This structure ensures successful segmentation results by preserving local information. In digital pathology, previous studies have used the U-Net structure to segment various cancers, such as in breast [ 14 , 15 , 16 , 17 , 18 ], colon [ 19 , 20 , 21 , 22 , 23 ], lung [ 24 , 25 ], and prostate tissue [ 26 , 27 ]. Furthermore, the structure is also applied to localized tissues such as the nuclei [ 24 , 28 , 29 , 30 , 31 , 32 ], cells [ 33 , 34 ] and glands [ 19 , 20 , 21 , 22 , 23 ], which exhibit major pathological characteristics.…”

Section: Related Workmentioning

confidence: 99%

A Novel Method Based on GAN Using a Segmentation Module for Oligodendroglioma Pathological Image Generation

Kweon

Yoo

Kim³

et al. 2022

Sensors

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Digital pathology analysis using deep learning has been the subject of several studies. As with other medical data, pathological data are not easily obtained. Because deep learning-based image analysis requires large amounts of data, augmentation techniques are used to increase the size of pathological datasets. This study proposes a novel method for synthesizing brain tumor pathology data using a generative model. For image synthesis, we used embedding features extracted from a segmentation module in a general generative model. We also introduce a simple solution for training a segmentation model in an environment in which the masked label of the training dataset is not supplied. As a result of this experiment, the proposed method did not make great progress in quantitative metrics but showed improved results in the confusion rate of more than 70 subjects and the quality of the visual output.

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“…The grand challenge CAMELYON17 has 37 algorithms to predict at the WSI level using 899 learnable WSIs, where the top performance from the GoogleNet was reproduced based on the multi-scale and color normalization ( [20]), which with rela-tively shadow architecture and relatively longer training phase than others. After the patch level computation with CNN, this top method used the conventional machine learning and feature engineering to classify WSIs, which was surpassed by PFA-ScanNet that using more scales to extract features ( [21]), then it was surpassed by the novel attention-based classifier with a shadower siamese MI-FCN architecture ( [22]). For the limited learnable data at the patch level, weakly supervised learning is earlier employed for histopathological WSI level prediction by [23], authors developed the multiple instance learning (MIL)based method on the clinical application and achieved the better performance than pathologists with a huge 44,732 WSIs dataset.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Cervical Whole Slide Image Analysis Framework Based on Multi-scale Semantic and Spatial Deep Features

Wei¹,

Cheng²,

Hu³

et al. 2021

Preprint

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Digital gigapixel whole slide image (WSI) is widely used in clinical diagnosis, and automated WSI analysis is key for computer-aided diagnosis. Currently, analyzing the integrated descriptor of probabilities or feature maps from massive local patches encoded by ResNet classifier is the main manner for WSI-level prediction. Feature representations of the sparse and tiny lesion cells in cervical slides, however, are still challengeable for the under-promoted upstream encoders, while the unused spatial representations of cervical cells are the available features to supply the semantics analysis. As well as patches sampling with overlap and repetitive processing incur the inefficiency and the unpredictable side effect. This study designs a novel inline connection network (InCNet) by enriching the multi-scale connectivity to build the lightweight model named You Only Look Cytopathology Once (YOLCO) with the additional supervision of spatial information. The proposed model allows the input size enlarged to megapixel that can stitch the WSI without any overlap by the average repeats decreased from 10 3 ∼ 10 4 to 10 1 ∼ 10 2 for collecting features and predictions at two scales. Based on Transformer for classifying the integrated multi-scale multi-task features, the experimental results appear 0.872 AUC score better and 2.51× faster than the best conventional method in WSI classification on multicohort datasets of 2,019 slides from four scanning devices.

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PFA-ScanNet: Pyramidal Feature Aggregation with Synergistic Learning for Breast Cancer Metastasis Analysis

Cited by 19 publications

References 14 publications

Progress on deep learning in digital pathology of breast cancer: a narrative review

Progress on deep learning in digital pathology of breast cancer: a narrative review

A Novel Method Based on GAN Using a Segmentation Module for Oligodendroglioma Pathological Image Generation

An Efficient Cervical Whole Slide Image Analysis Framework Based on Multi-scale Semantic and Spatial Deep Features

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