A Deep Learning Approach for Breast Invasive Ductal Carcinoma Detection and Lymphoma Multi-Classification in Histological Images

Brancati, Nadia; Pietro, Giuseppe De; Frucci, Maria; Riccio, Daniel

doi:10.1109/access.2019.2908724

Cited by 69 publications

(43 citation statements)

References 22 publications

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“…For patientlevel diagnosis, in turn, MuDeRN achieved an accuracy of 96.25%, considering the eight classes. Brancati et al [143] also used a ResNet to detect invasive ductal carcinoma as well as to classify lymphoma subtypes. First, convolutional layers are trained without supervision to learn a latent representation to reconstruct the input image.…”

Section: Methods Based On Deep Learning (Dl)mentioning

confidence: 99%

Machine Learning Methods for Histopathological Image Analysis: A Review

et al. 2021

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Histopathological images (HIs) are the gold standard for evaluating some types of tumors for cancer diagnosis. The analysis of such images is time and resource-consuming and very challenging even for experienced pathologists, resulting in inter-observer and intra-observer disagreements. One of the ways of accelerating such an analysis is to use computer-aided diagnosis (CAD) systems. This paper presents a review on machine learning methods for histopathological image analysis, including shallow and deep learning methods. We also cover the most common tasks in HI analysis, such as segmentation and feature extraction. Besides, we present a list of publicly available and private datasets that have been used in HI research.

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Section: Methods Based On Deep Learning (Dl)mentioning

confidence: 99%

Machine Learning Methods for Histopathological Image Analysis: A Review

et al. 2021

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“…More recently, researchers have looked into unsupervised methods of deep learning for the detection of BCa and components of histopathology tissue [75][76][77][78][79]. [75] made use of FusionNet [80], a form of a Convolutional Autoencoder (CAE), that made use of very long skip connections between the encoder and decoder subnets to generate images -similar to those done by generative models in machine learning [81]. As done predominantly elsewhere, they used patches of WSIs for detection of IDC by only training the encoder network of the FusionNet and running a softmax classi er to obtain binary outputs.…”

Section: Unsupervised Deep Learning-based Approachesmentioning

confidence: 99%

On the dynamics and feasibility of transferred inference for diagnosis of invasive ductal carcinoma: A perspective

Harshvardhan

Sahu

Gourisaria

et al. 2021

Preprint

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It is generally noticed that increasing the number of convolutional layers in generic image classification procedures proves to be detrimental to model performance in terms of validation accuracy and loss. Apart from vanilla CNNs, we have state-of-the-art (SOTA) architectures such as ResNet50 (and its variants) which show that through the use of skip-connections, higher performance metrics are attainable through deeper architectures. However, most evaluative metrics converge on a log scale as we go deeper with diminishing gradient of the metrics' curves. Given these two contrasting speculations, in this paper, we implement various vanilla and SOTA CNNs for the diagnosis of one of the most common forms of breast cancer - invasive ductal carcinoma (IDC) - to examine and understand the feasibility of implementation of SOTA CNNs through transferred weights when juxtaposed with vanilla CNNs (and LeNet-5) of varying configurations in terms of their performance metrics and other parameters. In this paper, we solve the dual-objective of studying behavioural aspects of avant-garde CNN models (more specifically, VGG16, VGG19, ResNet50, ResNet50V2, MobileNetV2, and DenseNet121) and proper diagnosis of IDC through intermediate neural activations to critically evaluate and theorize the performance of different models. We notice that among all the models, only VGG16, VGG19, LeNet-5 and a selected vanilla CNN through an optimization procedure were the ones to attain the best metrics, shared amongst them.

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“…A large number of scholars have conducted in‐depth research on the auxiliary diagnosis of lymphoma with DL and have made substantial progress. Brancati et al 10 provided a Supervised Encoder FusionNet (SEF) model for NHL subtype classification. The authors zoomed out 374 groups of pathological images, sized 1388 × 1040 × 3 pixels to 170 × 128 × 3 pixels, and used the central parts of the scaled images (128 × 128 × 3 pixels) as the model input.…”

Section: Related Workmentioning

confidence: 99%

Classification of digital pathological images of non‐Hodgkin's lymphoma subtypes based on the fusion of transfer learning and principal component analysis

et al. 2020

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Purpose: Non-Hodgkin's lymphoma (NHL) is a serious malignant disease. Delayed diagnosis will cause anemia, increased intracranial pressure, organ failure, and even lead to death. The current main trend in this area is to use deep learning (DL) for disease diagnosis. Extracting classification information from the digital pathology images by DL may realize the automated qualitative and quantitative analysis of NHL. Previously, DL has been used to classify NHL digital pathology images with some success. However, shortcomings still exist in the data preprocessing methods and feature extraction. Therefore, this paper presents a method for the classification of NHL subtypes based on the fusion of transfer learning (TL) and principal component analysis (PCA). Methods: First, the NHL digital pathology images were preprocessed by image division and segmentation and then input into the transfer models for fine-tuning and feature extraction. Second, PCA was used to map the extracted features. Finally, a neural network was used as a classifier to classify the mapped features. During the fine-tuning of the transfer models, two methods, freezing all feature extraction layers and fine-tuning all layers, were employed to select the optimal model with the best classification result among all the preselected transfer models. On this basis, the use of freezing the layers' location was discussed and analyzed. Results: The results show that the proposed method achieved average fivefold cross-validation accuracies of 100%, 99.73%, and 99.20% for chronic lymphocytic leukemia (CLL), follicular lymphoma (FL), and mantle cell lymphoma (MCL) tumor, and each category has standard deviations 0.00, 0.53, and 0.65, respectively, in the NHL reference dataset. The overall classification accuracy for fivefold cross-validation is 98.93%, which is an increase of 1.26% compared to the latest reported methods, having a lower standard deviation (1.00). Conclusion: The method proposed in this paper achieves a high classification accuracy and strong model generalization for the classification of NHL, which makes it possible to conduct intelligent classification of NHL in clinical practice. Our proposed method has definite clinical value and research significance.

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A Deep Learning Approach for Breast Invasive Ductal Carcinoma Detection and Lymphoma Multi-Classification in Histological Images

Cited by 69 publications

References 22 publications

Machine Learning Methods for Histopathological Image Analysis: A Review

Machine Learning Methods for Histopathological Image Analysis: A Review

On the dynamics and feasibility of transferred inference for diagnosis of invasive ductal carcinoma: A perspective

Classification of digital pathological images of non‐Hodgkin's lymphoma subtypes based on the fusion of transfer learning and principal component analysis

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