AI Medical Compendium Topic:
Breast Neoplasms

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A Parallel Spiking Neural Network Based on Adaptive Lateral Inhibition Mechanism for Objective Recognition.

Computational intelligence and neuroscience
Spiking neural network (SNN) has attracted extensive attention in the field of machine learning because of its biological interpretability and low power consumption. However, the accuracy of pattern recognition cannot completely surpass deep neural n...

Attention-based Deep Learning for the Preoperative Differentiation of Axillary Lymph Node Metastasis in Breast Cancer on DCE-MRI.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Previous studies have explored the potential on radiomics features of primary breast cancer tumor to identify axillary lymph node (ALN) metastasis. However, the value of deep learning (DL) to identify ALN metastasis remains unclear.

Classification of Multiclass Histopathological Breast Images Using Residual Deep Learning.

Computational intelligence and neuroscience
Pathologists need a lot of clinical experience and time to do the histopathological investigation. AI may play a significant role in supporting pathologists and resulting in more accurate and efficient histopathological diagnoses. Breast cancer is on...

A Deep Learning Method for Breast Cancer Classification in the Pathology Images.

IEEE journal of biomedical and health informatics
Breast cancer is the most common female cancer in the world, and it poses a huge threat to women's health. There is currently promising research concerning its early diagnosis using deep learning methodologies. However, some commonly used Convolution...

Breast MRI Segmentation and Ki-67 High- and Low-Expression Prediction Algorithm Based on Deep Learning.

Computational and mathematical methods in medicine
RESULTS: The DSC, PPV, and sensitivity of our combined model are 0.94, 0.93, and 0.94, respectively, with better segmentation performance. And we compare with the segmentation frameworks of other papers and find that our combined model can make accur...

FBCU-Net: A fine-grained context modeling network using boundary semantic features for medical image segmentation.

Computers in biology and medicine
The performance of deep learning-based medical image segmentation methods largely depends on the segmentation accuracy of tissue boundaries. However, since the boundary region is at the junction of areas of different categories, the pixels located at...

MTRRE-Net: A deep learning model for detection of breast cancer from histopathological images.

Computers in biology and medicine
Histopathological image classification has become one of the most challenging tasks among researchers due to the fine-grained variability of the disease. However, the rapid development of deep learning-based models such as the Convolutional Neural Ne...

Deep Learning Approaches for Detection of Breast Adenocarcinoma Causing Carcinogenic Mutations.

International journal of molecular sciences
Genes are composed of DNA and each gene has a specific sequence. Recombination or replication within the gene base ends in a permanent change in the nucleotide collection in a DNA called mutation and some mutations can lead to cancer. Breast adenocar...

Improving Performance of Breast Lesion Classification Using a ResNet50 Model Optimized with a Novel Attention Mechanism.

Tomography (Ann Arbor, Mich.)
Background: The accurate classification between malignant and benign breast lesions detected on mammograms is a crucial but difficult challenge for reducing false-positive recall rates and improving the efficacy of breast cancer screening. Objective:...