Papers › Breast Cancer Diagnosis in Two-View Mammography Using End-to-End Trained...

Breast Cancer Diagnosis in Two-View Mammography Using End-to-End Trained EfficientNet-Based Convolutional Network

1 Oct 2021IEEE Access 2022 7arXiv:2110.01606archive 2025-07-28

Daniel G. P. Petrini, Carlos Shimizu, Rosimeire A. Roela, Gabriel V. Valente, Maria A. A. K. Folgueira, Hae Yong Kim

Some recent studies have described deep convolutional neural networks to diagnose breast cancer in mammograms with similar or even superior performance to that of human experts. One of the best techniques does two transfer learnings: the first uses a model trained on natural images to create a "patch classifier" that categorizes small subimages; the second uses the patch classifier to scan the whole mammogram and create the "single-view whole-image classifier". We propose to make a third transfer learning to obtain a "two-view classifier" to use the two mammographic views: bilateral craniocaudal and mediolateral oblique. We use EfficientNet as the basis of our model. We "end-to-end" train the entire system using CBIS-DDSM dataset. To ensure statistical robustness, we test our system twice using: (a) 5-fold cross validation; and (b) the original training/test division of the dataset. Our technique reached an AUC of 0.9344 using 5-fold cross validation (accuracy, sensitivity and specificity are 85.13% at the equal error rate point of ROC). Using the original dataset division, our technique achieved an AUC of 0.8483, as far as we know the highest reported AUC for this problem, although the subtle differences in the testing conditions of each work do not allow for an accurate comparison. The inference code and model are available at https://github.com/dpetrini/two-views-classifier

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Tasks

Cancer-no cancer per breast classification Cancer-no cancer per image classification Specificity Transfer Learning

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Cancer-no cancer per breast classification	CBIS-DDSM	EfficientNet-B0 w/ TTA	AUC	0.8483	#1 of 3	Archive leaderboard	report
Cancer-no cancer per breast classification	CBIS-DDSM	EfficientNet-B0	AUC	0.8418	#2 of 3	Archive leaderboard	report
Cancer-no cancer per image classification	CBIS-DDSM	SingleView_PatchBased_EfficientNet-B0	AUC	0.8033	#2 of 16	Archive leaderboard	report
Cancer-no cancer per image classification	CBIS-DDSM	SingleView_PatchBased_EfficientNet-B3	AUC	0.7952	#5 of 16	Archive leaderboard	report
Cancer-no cancer per image classification	CBIS-DDSM	VGG/ResNet	AUC	0.75	#13 of 16	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

1x1 Convolution Average Pooling Batch Normalization Convolution Dense Connections Depthwise Convolution Depthwise Separable Convolution Dropout Inverted Residual Block Pointwise Convolution RMSProp ReLU Sigmoid Activation Squeeze-and-Excitation Block Test

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