Adaptation of a deep learning malignancy model from full-field digital mammography to digital breast tomosynthesis

Singh, Sadanand; Matthews, Thomas P.; Shah, Meet; Mombourquette, Brent; Tsue, Trevor; Long, Aaron; Almohsen, Ranya; Pedemonte, Stefano; Su, Jason

doi:10.1117/12.2549923

Cited by 18 publications

(16 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The authors [ 14 ] used a deep learning model that has been trained on FFDM images and then modify it to operate with DBT images. These images were divided into four categories according to their FFDM and DBT content.…”

Section: Related Workmentioning

confidence: 99%

“…This problem can be somewhat resolved if you think of a tomographic image and perform any analysis. Many studies have shown that using a 2D radiomics technique can get comparable results to radiomic analysis [ 12 ] of a straightforward mathematical formulation for radiomic properties [ 13 , 14 ]. Due to the fact that the information is being supplemented in a 2D manner, it is possible to create robust diagnostic classifiers.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Dense Convolutional Neural Network for Detection of Cancer from CT Images

Sreenivasu¹,

Gomathi²,

Kumar

et al. 2022

BioMed Research International

View full text Add to dashboard Cite

In this paper, we develop a detection module with strong training testing to develop a dense convolutional neural network model. The model is designed in such a way that it is trained with necessary features for optimal modelling of the cancer detection. The method involves preprocessing of computerized tomography (CT) images for optimal classification at the testing stages. A 10-fold cross-validation is conducted to test the reliability of the model for cancer detection. The experimental validation is conducted in python to validate the effectiveness of the model. The result shows that the model offers robust detection of cancer instances that novel approaches on large image datasets. The simulation result shows that the proposed method provides analyzes with 94% accuracy than other methods. Also, it helps to reduce the detection errors while classifying the cancer instances than other methods the several existing methods.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dense Convolutional Neural Network for Detection of Cancer from CT Images

Sreenivasu¹,

Gomathi²,

Kumar

et al. 2022

BioMed Research International

View full text Add to dashboard Cite

show abstract

“…To this end, we compare SpotTUnet with the best unlearnable layer choice strategies within the supervised DA setup and show it to be a reliable tool for domain shift analysis. While authors of [13] demonstrate SpotTune to perform worse than histogram matching preprocessing in the medical image classification task, we argue that histogram matching is a task-specific method and show its extremely poor segmentation quality in our task. Many approaches competitive to SpotTune have been developed recently, but their focus is more narrow: obtaining the best score on the Target domain rather than analyzing domain shift properties.…”

Section: Related Workmentioning

confidence: 64%

“…Supervised DA methods On the rest of the 25 testing pairs, we compare 4 methods: fine-tuning of the first network layers, fine-tuning of the whole network from [12], histogram matching from [13], and SpotTUnet. We load a baseline model pretrained on the corresponding Source domain and then fine-tune it via one of the methods or preprocess the Target data in case of histogram matching.…”

Section: Methodsmentioning

confidence: 99%

Anatomy of Domain Shift Impact on U-Net Layers in MRI Segmentation

Zakazov¹,

Shirokikh²,

Chernyavskiy³

et al. 2021

Preprint

View full text Add to dashboard Cite

Domain Adaptation (DA) methods are widely used in medical image segmentation tasks to tackle the problem of differently distributed train (source) and test (target) data. We consider the supervised DA task with a limited number of annotated samples from the target domain. It corresponds to one of the most relevant clinical setups: building a sufficiently accurate model on the minimum possible amount of annotated data. Existing methods mostly fine-tune specific layers of the pretrained Convolutional Neural Network (CNN). However, there is no consensus on which layers are better to fine-tune, e.g. the first layers for images with low-level domain shift or the deeper layers for images with high-level domain shift. To this end, we propose SpotTUnet -a CNN architecture that automatically chooses the layers which should be optimally fine-tuned. More specifically, on the target domain, our method additionally learns the policy that indicates whether a specific layer should be fine-tuned or reused from the pretrained network. We show that our method performs at the same level as the best of the nonflexible fine-tuning methods even under the extreme scarcity of annotated data. Secondly, we show that SpotTUnet policy provides a layer-wise visualization of the domain shift impact on the network, which could be further used to develop robust domain generalization methods. In order to extensively evaluate SpotTUnet performance, we use a publicly available dataset of brain MR images (CC359), characterized by explicit domain shift. We release a reproducible experimental pipeline 3 .

show abstract

“…When compared to stacked CNN, the addition of residual blocks in the network increases representation power, leads to faster convergence, and lowers training errors [ 82 ]. A work by Singh et al [ 83 ] proposed a pre-trained model with FFDM images, which was utilized for DBT images. This study used two fine-tuning methods: (1) fine-tuning the last two layers and (2) fine-tuning only the optimal layers.…”

Section: Breast-cancer-diagnosis Methods Based On Deep Learningmentioning

confidence: 99%

A Comprehensive Survey on Deep-Learning-Based Breast Cancer Diagnosis

Mridha

Hamid

Monowar

et al. 2021

Cancers

View full text Add to dashboard Cite

Breast cancer is now the most frequently diagnosed cancer in women, and its percentage is gradually increasing. Optimistically, there is a good chance of recovery from breast cancer if identified and treated at an early stage. Therefore, several researchers have established deep-learning-based automated methods for their efficiency and accuracy in predicting the growth of cancer cells utilizing medical imaging modalities. As of yet, few review studies on breast cancer diagnosis are available that summarize some existing studies. However, these studies were unable to address emerging architectures and modalities in breast cancer diagnosis. This review focuses on the evolving architectures of deep learning for breast cancer detection. In what follows, this survey presents existing deep-learning-based architectures, analyzes the strengths and limitations of the existing studies, examines the used datasets, and reviews image pre-processing techniques. Furthermore, a concrete review of diverse imaging modalities, performance metrics and results, challenges, and research directions for future researchers is presented.

show abstract

Adaptation of a deep learning malignancy model from full-field digital mammography to digital breast tomosynthesis

Cited by 18 publications

References 13 publications

Dense Convolutional Neural Network for Detection of Cancer from CT Images

Dense Convolutional Neural Network for Detection of Cancer from CT Images

Anatomy of Domain Shift Impact on U-Net Layers in MRI Segmentation

A Comprehensive Survey on Deep-Learning-Based Breast Cancer Diagnosis

Contact Info

Product

Resources

About