AIMC Topic: Breast Neoplasms

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Deep learning-based cross-classifications reveal conserved spatial behaviors within tumor histological images.

Nature communications
Histopathological images are a rich but incompletely explored data type for studying cancer. Manual inspection is time consuming, making it challenging to use for image data mining. Here we show that convolutional neural networks (CNNs) can be system...

New Approach for Risk Estimation Algorithms of Negativeness Detection with Modelling Supervised Machine Learning Techniques.

Disease markers
gene testing is a difficult, expensive, and time-consuming test which requires excessive work load. The identification of the gene mutations is significantly important in the selection of treatment and the risk of secondary cancer. We aimed to deve...

An end-to-end breast tumour classification model using context-based patch modelling - A BiLSTM approach for image classification.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Researchers working on computational analysis of Whole Slide Images (WSIs) in histopathology have primarily resorted to patch-based modelling due to large resolution of each WSI. The large resolution makes WSIs infeasible to be fed directly into the ...

Multi-path synergic fusion deep neural network framework for breast mass classification using digital breast tomosynthesis.

Physics in medicine and biology
OBJECTIVE: To develop and evaluate a multi-path synergic fusion (MSF) deep neural network model for breast mass classification using digital breast tomosynthesis (DBT).

Residual breast tissue after robot-assisted nipple sparing mastectomy.

Breast (Edinburgh, Scotland)
INTRODUCTION: While the long-term oncologic safety of robot-assisted nipple sparing mastectomy (RNSM) remains to be elucidated, histologically detected residual breast tissue (RBT) can be a surrogate for oncologically sound mastectomy. The objective ...

Early prediction of neoadjuvant chemotherapy response for advanced breast cancer using PET/MRI image deep learning.

Scientific reports
This study aimed to investigate the predictive efficacy of positron emission tomography/computed tomography (PET/CT) and magnetic resonance imaging (MRI) for the pathological response of advanced breast cancer to neoadjuvant chemotherapy (NAC). The b...

Breast Tumor Classification in Ultrasound Images Using Combined Deep and Handcrafted Features.

Sensors (Basel, Switzerland)
This study aims to enable effective breast ultrasound image classification by combining deep features with conventional handcrafted features to classify the tumors. In particular, the deep features are extracted from a pre-trained convolutional neura...

Classification of malignant tumors in breast ultrasound using a pretrained deep residual network model and support vector machine.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
In this study, a transfer learning method was utilized to recognize and classify benign and malignant breast tumors, using two-dimensional breast ultrasound (US) images, to decrease the effort expended by physicians and improve the quality of clinica...

Machine learning techniques for mitoses classification.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
BACKGROUND: Pathologists analyze biopsy material at both the cellular and structural level to determine diagnosis and cancer stage. Mitotic figures are surrogate biomarkers of cellular proliferation that can provide prognostic information; thus, thei...