Signed Laplacian Deep Learning with Adversarial Augmentation for Improved Mammography Diagnosis

Li, Heyi; Chen, Dongdong; Nailon, William H.; Davies, Mike E.; Laurenson, David

doi:10.1007/978-3-030-32226-7_54

Cited by 11 publications

(12 citation statements)

References 17 publications

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“…Consequently, the intraclass difference is minimized, and more importantly, the interclass manifold margin is maximized in the deep representation space. A preliminary version of this work appeared in [27]. This paper extends [27] by discussion and experiments so as to prove the effectiveness of our motivation for solving data scarcity (Q1) and data entanglement (Q2).…”

Section: Our Contributionmentioning

confidence: 74%

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COIN: Contrastive Identifier Network for Breast Mass Diagnosis in Mammography

Li,

Chen,

Nailon

et al. 2020

Preprint

Self Cite

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Computer-aided breast cancer diagnosis in mammography is a challenging problem, stemming from mammographical data scarcity and data entanglement. In particular, data scarcity is attributed to the privacy and expensive annotation. And data entanglement is due to the high similarity between benign and malignant masses, of which manifolds reside in lower dimensional space with very small margin. To address these two challenges, we propose a deep learning framework, named Contrastive Identifier Network (COIN), which integrates adversarial augmentation and manifold-based contrastive learning. Firstly, we employ adversarial learning to create both on-and off-distribution mass contained ROIs. After that, we propose a novel contrastive loss with a built Signed graph. Finally, the neural network is optimized in a contrastive learning manner, with the purpose of improving the deep model's discriminativity on the extended dataset. In particular, by employing COIN, data samples from the same category are pulled close whereas those with different labels are pushed further in the deep latent space. Moreover, COIN outperforms the state-of-the-art related algorithms for solving breast cancer diagnosis problem by a considerable margin, achieving 93.4% accuracy and 95.0% AUC score. The code will release on ***.

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Section: Our Contributionmentioning

confidence: 74%

“…A preliminary version of this work appeared in [27]. This paper extends [27] by discussion and experiments so as to prove the effectiveness of our motivation for solving data scarcity (Q1) and data entanglement (Q2).…”

Section: Our Contributionmentioning

confidence: 74%

COIN: Contrastive Identifier Network for Breast Mass Diagnosis in Mammography

Li,

Chen,

Nailon

et al. 2020

Preprint

Self Cite

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“…Area Under the Curve (AUC) is used as the evaluation metric in image-wise. For implementation of compared baselines, we directly load the published codes of ERM [10], Chen et al [6], DANN [9], MMD-AAE [18], DIVA [11], IRM [1] and Prithvijit et al [5] during test; while we re-implement methods of Guided-VAE [8], ICADx [13] and Li et al [19] for lacking published source codes.…”

Section: Methodsmentioning

confidence: 99%

“…Patch-Level Mammogram Mass Classification. Previous approaches that can be used to address patch-level mammogram mass benign/malignant classification without ROI annotations are roughly categorized into three classes: (i) the GAN-based methods, e.g., Li et al [19]; (ii) the disentanglingbased methods, e.g., Guided-VAE [8]; (iii) the attribute-guided methods, e.g., Chen et al [6], ICADx [13]. For class (i), they propose an adversarial generation to augment training data for better prediction.…”

Section: Related Workmentioning

confidence: 99%

Domain Invariant Model with Graph Convolutional Network for Mammogram Classification

Wang¹,

Li²,

Sun³

et al. 2022

Preprint

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Due to its safety-critical property, the image-based diagnosis is desired to achieve robustness on out-of-distribution (OOD) samples. A natural way towards this goal is capturing only clinically disease-related features, which is composed of macroscopic attributes (e.g., margins, shapes) and microscopic image-based features (e.g., textures) of lesion-related areas. However, such disease-related features are often interweaved with data-dependent (but disease irrelevant) biases during learning, disabling the OOD generalization. To resolve this problem, we propose a novel framework, namely Domain Invariant Model with Graph Convolutional Network (DIM-GCN), which only exploits invariant disease-related features from multiple domains. Specifically, we first propose a Bayesian network, which explicitly decomposes the latent variables into disease-related and other disease-irrelevant parts that are provable to be disentangled from each other. Guided by this, we reformulate the objective function based on Variational Auto-Encoder, in which the encoder in each domain has two branches: the domain-independent and -dependent ones, which respectively encode disease-related and -irrelevant features. To better capture the macroscopic features, we leverage the observed clinical attributes as a goal for reconstruction, via Graph Convolutional Network (GCN). Finally, we only implement the disease-related features for prediction. The effectiveness and utility of our method are demonstrated by the superior OOD generalization performance over others on mammogram benign/malignant diagnosis.

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“…For instance in [24] a Euclidian magnitude regularization approach is proposed in a deep learning pipeline for mammogram mass segmentation. More recently, adversarial augmentation combined with graph-based regularization [40] has been proposed improve the model's generalization for mammogram diagnosis.…”

Section: Transfer Learning and Data Augmentation For Mammogram Classi...mentioning

confidence: 99%

A real use case of semi-supervised learning for mammogram classification in a local clinic of Costa Rica

Calderón-Ramírez

Murillo-Hernandez

Rojas-Salazar

et al. 2022

Med Biol Eng Comput

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The implementation of deep learning-based computer-aided diagnosis systems for the classification of mammogram images can help in improving the accuracy, reliability, and cost of diagnosing patients. However, training a deep learning model requires a considerable amount of labelled images, which can be expensive to obtain as time and effort from clinical practitioners are required. To address this, a number of publicly available datasets have been built with data from different hospitals and clinics, which can be used to pre-train the model. However, using models trained on these datasets for later transfer learning and model fine-tuning with images sampled from a different hospital or clinic might result in lower performance. This is due to the distribution mismatch of the datasets, which include different patient populations and image acquisition protocols. In this work, a real-world scenario is evaluated where a novel target dataset sampled from a private Costa Rican clinic is used, with few labels and heavily imbalanced data. The use of two popular and publicly available datasets (INbreast and CBIS-DDSM) as source data, to train and test the models on the novel target dataset, is evaluated. A common approach to further improve the model's performance under such small labelled target dataset setting is data augmentation. However, often cheaper unlabelled data is available from the target clinic. Therefore, semi-supervised deep learning, which leverages both labelled and unlabelled data, can be used in such conditions. In this work, we evaluate the semi-supervised deep learning approach known as MixMatch, to take advantage of unlabelled data from the target dataset, for whole mammogram image classification. We compare the usage of semi-supervised learning on its own, and combined with transfer learning (from a source mammogram dataset) with data augmentation, as also against regular supervised learning with transfer learning and data augmentation from source datasets. It is shown that the use of a semi-supervised deep learning combined with transfer learning and data augmentation can provide a meaningful advantage when using scarce labelled observations. Also, we found a strong influence of the source dataset, which suggests a more data-centric approach needed to tackle the challenge of scarcely labelled data. We used several different metrics to assess the performance gain of using semi-supervised learning, when dealing with very imbalanced test datasets (such as the G-mean and the F2-score), as mammogram datasets are often very imbalanced.

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Signed Laplacian Deep Learning with Adversarial Augmentation for Improved Mammography Diagnosis

Cited by 11 publications

References 17 publications

COIN: Contrastive Identifier Network for Breast Mass Diagnosis in Mammography

COIN: Contrastive Identifier Network for Breast Mass Diagnosis in Mammography

Domain Invariant Model with Graph Convolutional Network for Mammogram Classification

A real use case of semi-supervised learning for mammogram classification in a local clinic of Costa Rica

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