Weakly-Supervised Classification of HER2 Expression in Breast Cancer Haematoxylin and Eosin Stained Slides

Oliveira, Sara P.; Pinto, João Ribeiro; Gonçalves, Tiago; Canas-Marques, Rita; Cardoso, Maria João; Oliveira, Hélder; Cardoso, Jaime S.

doi:10.3390/app10144728

Cited by 17 publications

(11 citation statements)

References 31 publications

(42 reference statements)

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“…Moreover, labelling the tiles represents a significant effort with regards to the workload of the pathologists. Therefore, techniques such as multiple instance learning (MIL), have been adapted to computational pathology problems [68][69][70] . MIL only requires slide level labels and the original supervised problem is converted to a weakly-supervised problem.…”

Section: Methodsmentioning

confidence: 99%

CAD systems for colorectal cancer from WSI are still not ready for clinical acceptance

Oliveira

Neto

Fraga³

et al. 2021

Sci Rep

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Most oncological cases can be detected by imaging techniques, but diagnosis is based on pathological assessment of tissue samples. In recent years, the pathology field has evolved to a digital era where tissue samples are digitised and evaluated on screen. As a result, digital pathology opened up many research opportunities, allowing the development of more advanced image processing techniques, as well as artificial intelligence (AI) methodologies. Nevertheless, despite colorectal cancer (CRC) being the second deadliest cancer type worldwide, with increasing incidence rates, the application of AI for CRC diagnosis, particularly on whole-slide images (WSI), is still a young field. In this review, we analyse some relevant works published on this particular task and highlight the limitations that hinder the application of these works in clinical practice. We also empirically investigate the feasibility of using weakly annotated datasets to support the development of computer-aided diagnosis systems for CRC from WSI. Our study underscores the need for large datasets in this field and the use of an appropriate learning methodology to gain the most benefit from partially annotated datasets. The CRC WSI dataset used in this study, containing 1,133 colorectal biopsy and polypectomy samples, is available upon reasonable request.

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Section: Methodsmentioning

confidence: 99%

CAD systems for colorectal cancer from WSI are still not ready for clinical acceptance

Oliveira

Neto

Fraga³

et al. 2021

Sci Rep

Self Cite

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“…Oliveira et al [ 25 ] developed a CNN model based on multiple instance learning (MIL) approaches identifying HER2 status from H&E images. Initially, the CNN model was pre-trained from IHC images on the HER2SC dataset.…”

Section: Related Workmentioning

confidence: 99%

Strategies for Enhancing the Multi-Stage Classification Performances of HER2 Breast Cancer from Hematoxylin and Eosin Images

et al. 2022

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Breast cancer is a significant health concern among women. Prompt diagnosis can diminish the mortality rate and direct patients to take steps for cancer treatment. Recently, deep learning has been employed to diagnose breast cancer in the context of digital pathology. To help in this area, a transfer learning-based model called ‘HE-HER2Net’ has been proposed to diagnose multiple stages of HER2 breast cancer (HER2-0, HER2-1+, HER2-2+, HER2-3+) on H&E (hematoxylin & eosin) images from the BCI dataset. HE-HER2Net is the modified version of the Xception model, which is additionally comprised of global average pooling, several batch normalization layers, dropout layers, and dense layers with a swish activation function. This proposed model exceeds all existing models in terms of accuracy (0.87), precision (0.88), recall (0.86), and AUC score (0.98) immensely. In addition, our proposed model has been explained through a class-discriminative localization technique using Grad-CAM to build trust and to make the model more transparent. Finally, nuclei segmentation has been performed through the StarDist method.

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“…BC image analysis can be applied to handle numerous pathology jobs, such as mitosis detection [18,23], tissue segmentation [24], histological classification or cancer grading [15]. Automatic analysis is commonly performed by using hematoxylin and eosin (H&E)-stained slides and deep learning techniques to enhance model performance [25]. Breast cancer diagnosis from histopathological images always remains the benchmark in clinical pathology [20][21][22][23][24].…”

Section: Automated Image Analysis In Histopathologymentioning

confidence: 99%

Automated Grading of Breast Cancer Histopathology Images Using Multilayered Autoencoder

Mehak¹,

Ashraf²,

Zafar³

et al. 2022

Computers, Materials &Amp; Continua

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Breast cancer (BC) is the most widely recognized cancer in women worldwide. By 2018, 627,000 women had died of breast cancer (World Health Organization Report 2018). To diagnose BC, the evaluation of tumours is achieved by analysis of histological specimens. At present, the Nottingham Bloom Richardson framework is the least expensive approach used to grade BC aggressiveness. Pathologists contemplate three elements, 1. mitotic count, 2. gland formation, and 3. nuclear atypia, which is a laborious process that witness's variations in expert's opinions. Recently, some algorithms have been proposed for the detection of mitotic cells, but nuclear atypia in breast cancer histopathology has not received much consideration. Nuclear atypia analysis is performed not only to grade BC but also to provide critical information in the discrimination of normal breast, non-invasive breast (usual ductal hyperplasia, atypical ductal hyperplasia) and pre-invasive breast (ductal carcinoma in situ) and invasive breast lesions. We proposed a deep-stacked multi-layer autoencoder ensemble with a softmax layer for the feature extraction and classification process. The classification results show the value of the multilayer autoencoder model in the evaluation of nuclear polymorphisms. The proposed method has indicated promising results, making them more fit in breast cancer grading.

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Weakly-Supervised Classification of HER2 Expression in Breast Cancer Haematoxylin and Eosin Stained Slides

Cited by 17 publications

References 31 publications

CAD systems for colorectal cancer from WSI are still not ready for clinical acceptance

CAD systems for colorectal cancer from WSI are still not ready for clinical acceptance

Strategies for Enhancing the Multi-Stage Classification Performances of HER2 Breast Cancer from Hematoxylin and Eosin Images

Automated Grading of Breast Cancer Histopathology Images Using Multilayered Autoencoder

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