Lightweight U-Net for High-Resolution Breast Imaging

Breast Cancer Classification from Histopathological Images with Inception Recurrent Residual Convolutional Neural Network

The Deep Convolutional Neural Network (DCNN) is one of the most powerful and successful deep learning approaches. DCNNs have already provided superior performance in different modalities of medical imaging including breast cancer classification, segmentation, and detection. Breast cancer is one of the most common and dangerous cancers impacting women worldwide. In this paper, we have proposed a method for breast cancer classification with the Inception Recurrent Residual Convolutional Neural Network (IRRCNN) model. The IRRCNN is a powerful DCNN model that combines the strength of the Inception…

HCMA-UNet：一种用于高效乳腺癌分割的混合CNN-Mamba UNet与轴向自注意力机制

HCMA-UNet: A Hybrid CNN-Mamba UNet with Axial Self-Attention for Efficient Breast Cancer Segmentation

本研究提出混合分割网络HCMA-UNet，结合轻量级CNN主干与多视图轴向自注意力Mamba模块，并引入特征引导区域感知损失函数，在DCE-MRI乳腺癌病灶分割任务中实现了计算高效的最先进性能与良好的跨架构泛化能力。

Photometric Transformer Networks and Label Adjustment for Breast Density Prediction

Grading breast density is highly sensitive to normalization settings of digital mammogram as the density is tightly correlated with the distribution of pixel intensity. Also, the grade varies with readers due to uncertain grading criteria. These issues are inherent in the density assessment of digital mammography. They are problematic when designing a computer-aided prediction model for breast density and become worse if the data comes from multiple sites. In this paper, we proposed two novel deep learning techniques for breast density prediction: 1) photometric transformation which adaptively…

Y-Net: Joint Segmentation and Classification for Diagnosis of Breast Biopsy Images

In this paper, we introduce a conceptually simple network for generating discriminative tissue-level segmentation masks for the purpose of breast cancer diagnosis. Our method efficiently segments different types of tissues in breast biopsy images while simultaneously predicting a discriminative map for identifying important areas in an image. Our network, Y-Net, extends and generalizes U-Net by adding a parallel branch for discriminative map generation and by supporting convolutional block modularity, which allows the user to adjust network efficiency without altering the network topology. Y-N…

Multi-Head Feature Pyramid Networks for Breast Mass Detection

Analysis of X-ray images is one of the main tools to diagnose breast cancer. The ability to quickly and accurately detect the location of masses from the huge amount of image data is the key to reducing the morbidity and mortality of breast cancer. Currently, the main factor limiting the accuracy of breast mass detection is the unequal focus on the mass boxes, leading the network to focus too much on larger masses at the expense of smaller ones. In the paper, we propose the multi-head feature pyramid module (MHFPN) to solve the problem of unbalanced focus of target boxes during feature map fus…

Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis

Breast cancer is one of the main causes of cancer death worldwide. Early diagnostics significantly increases the chances of correct treatment and survival, but this process is tedious and often leads to a disagreement between pathologists. Computer-aided diagnosis systems showed potential for improving the diagnostic accuracy. In this work, we develop the computational approach based on deep convolution neural networks for breast cancer histology image classification. Hematoxylin and eosin stained breast histology microscopy image dataset is provided as a part of the ICIAR 2018 Grand Challenge…

Breast Cancer Detection Using Convolutional Neural Networks

Breast cancer is prevalent in Ethiopia that accounts 34% among women cancer patients. The diagnosis technique in Ethiopia is manual which was proven to be tedious, subjective, and challenging. Deep learning techniques are revolutionizing the field of medical image analysis and hence in this study, we proposed Convolutional Neural Networks (CNNs) for breast mass detection so as to minimize the overheads of manual analysis. CNN architecture is designed for the feature extraction stage and adapted both the Region Proposal Network (RPN) and Region of Interest (ROI) portion of the faster R-CNN for …

CellLineNet: End-to-End Learning and Transfer Learning For Multiclass Epithelial Breast cell Line Classification via a Convolutional Neural Network

Computer Vision for Analyzing and Classifying cells and tissues often require rigorous lab procedures and so automated Computer Vision solutions have been sought. Most work in such field usually requires Feature Extractions before the analysis of such features via Machine Learning and Machine Vision algorithms. We developed a Convolutional Neural Network that classifies 5 types of epithelial breast cell lines comprised of two human cancer lines, 2 normal immortalized lines, and 1 immortalized mouse line (MDA-MB-468, MCF7, 10A, 12A and HC11) without requiring feature extraction. The Multiclass …

Improved Breast Mass Segmentation in Mammograms with Conditional Residual U-net

We explore the use of deep learning for breast mass segmentation in mammograms. By integrating the merits of residual learning and probabilistic graphical modelling with standard U-Net, we propose a new deep network, Conditional Residual U-Net (CRU-Net), to improve the U-Net segmentation performance. Benefiting from the advantage of probabilistic graphical modelling in the pixel-level labelling, and the structure insights of a deep residual network in the feature extraction, the CRU-Net provides excellent mass segmentation performance. Evaluations based on INbreast and DDSM-BCRP datasets demon…

Convolutional Neural Networks for Breast Cancer Screening: Transfer Learning with Exponential Decay

In this paper, we propose a Computer Assisted Diagnosis (CAD) system based on a deep Convolutional Neural Network (CNN) model, to build an end-to-end learning process that classifies breast mass lesions. We investigate the impact that has transfer learning when large data is scarce, and explore the proper way to fine-tune the layers to learn features that are more specific to the new data. The proposed approach showed better performance compared to other proposals that classified the same dataset.

PRISM-Net：用于三类乳腺DCE-MRI分类的患者特异性参考引导跨乳腺对称匹配

PRISM-Net: Patient-specific reference-guided inter-breast symmetry matching for three-class breast DCE-MRI classification

该研究针对乳腺DCE-MRI分类中患者特异性背景变异性问题，提出PRISM-Net无配准双侧框架，利用对侧乳腺特征建模跨乳腺对应，在多数据集上优于基线，提升了乳腺病灶分类性能。

Breast Tumor Segmentation and Shape Classification in Mammograms using Generative Adversarial and Convolutional Neural Network

Mammogram inspection in search of breast tumors is a tough assignment that radiologists must carry out frequently. Therefore, image analysis methods are needed for the detection and delineation of breast masses, which portray crucial morphological information that will support reliable diagnosis. In this paper, we proposed a conditional Generative Adversarial Network (cGAN) devised to segment a breast mass within a region of interest (ROI) in a mammogram. The generative network learns to recognize the breast mass area and to create the binary mask that outlines the breast mass. In turn, the ad…

Two-Stage Convolutional Neural Network for Breast Cancer Histology Image Classification

This paper explores the problem of breast tissue classification of microscopy images. Based on the predominant cancer type the goal is to classify images into four categories of normal, benign, in situ carcinoma, and invasive carcinoma. Given a suitable training dataset, we utilize deep learning techniques to address the classification problem. Due to the large size of each image in the training dataset, we propose a patch-based technique which consists of two consecutive convolutional neural networks. The first "patch-wise" network acts as an auto-encoder that extracts the most salient featur…

图神经网络用于乳房生物力学压缩建模

Graph Neural Networks for modelling breast biomechanical compression

本文首次将基于物理的图神经网络（PhysGNN）应用于乳房压缩模拟，利用网格结构和归纳学习捕捉复杂组织几何，训练于有限元分析数据，实现准确快速的变形预测，提升计算效率。

Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images

Automated classification of histopathological whole-slide images (WSI) of breast tissue requires analysis at very high resolutions with a large contextual area. In this paper, we present context-aware stacked convolutional neural networks (CNN) for classification of breast WSIs into normal/benign, ductal carcinoma in situ (DCIS), and invasive ductal carcinoma (IDC). We first train a CNN using high pixel resolution patches to capture cellular level information. The feature responses generated by this model are then fed as input to a second CNN, stacked on top of the first. Training of this stac…

MITOS-RCNN: A Novel Approach to Mitotic Figure Detection in Breast Cancer Histopathology Images using Region Based Convolutional Neural Networks

Studies estimate that there will be 266,120 new cases of invasive breast cancer and 40,920 breast cancer induced deaths in the year of 2018 alone. Despite the pervasiveness of this affliction, the current process to obtain an accurate breast cancer prognosis is tedious and time consuming, requiring a trained pathologist to manually examine histopathological images in order to identify the features that characterize various cancer severity levels. We propose MITOS-RCNN: a novel region based convolutional neural network (RCNN) geared for small object detection to accurately grade one of the thre…

Automatic Mass Detection in Breast Using Deep Convolutional Neural Network and SVM Classifier

Mammography is the most widely used gold standard for screening breast cancer, where, mass detection is considered as the prominent step. Detecting mass in the breast is, however, an arduous problem as they usually have large variations between them in terms of shape, size, boundary, and texture. In this literature, the process of mass detection is automated with the use of transfer learning techniques of Deep Convolutional Neural Networks (DCNN). Pre-trained VGG19 network is used to extract features which are then followed by bagged decision tree for features selection and then a Support Vect…

用于乳腺癌图像分类的混合量子卷积神经网络集成框架方法

An ensemble framework approach of hybrid Quantum convolutional neural networks for classification of breast cancer images

本文提出一种混合经典-量子神经网络集成框架，在乳腺癌组织病理学数据集上结合三种架构，集成后准确率达86.72%，优于单个混合模型与经典神经网络。

Cancer-Net BCa-S：使用来自合成相关扩散成像的体积深度影像组学特征进行乳腺癌分级预测

Cancer-Net BCa-S: Breast Cancer Grade Prediction using Volumetric Deep Radiomic Features from Synthetic Correlated Diffusion Imaging

针对传统乳腺癌SBR分级需活检的问题，提出基于合成相关扩散成像(CDI$^s$)体积深度影像组学方法Cancer-Net BCa-S，实现无需活检的准确SBR分级预测，性能优于金标准成像模态。

A Medical Pre-Diagnosis System for Histopathological Image of Breast Cancer

This paper constructs a novel intelligent medical diagnosis system, which can realize automatic communication and breast cancer pathological image recognition. This system contains two main parts, including a pre-training chatbot called M-Chatbot and an improved neural network model of EfficientNetV2-S named EfficientNetV2-SA, in which the activation function in top layers is replaced by ACON-C. Using information retrieval mechanism, M-Chatbot instructs patients to send breast pathological image to EfficientNetV2-SA network, and then the classifier trained by transfer learning will return the …

Deep learning approach for breast cancer diagnosis

Breast cancer is one of the leading fatal disease worldwide with high risk control if early discovered. Conventional method for breast screening is x-ray mammography, which is known to be challenging for early detection of cancer lesions. The dense breast structure produced due to the compression process during imaging lead to difficulties to recognize small size abnormalities. Also, inter- and intra-variations of breast tissues lead to significant difficulties to achieve high diagnosis accuracy using hand-crafted features. Deep learning is an emerging machine learning technology that requires…

PINN-EMFNet：基于PINN的增强多尺度特征融合网络用于乳腺超声图像分割

PINN-EMFNet: PINN-based and Enhanced Multi-Scale Feature Fusion Network for Breast Ultrasound Images Segmentation

针对乳腺超声图像对比度低、噪声大及肿瘤形态多变导致分割困难的问题，提出基于PINN的增强多尺度特征融合网络，通过分层聚合编码器、多尺度特征细化解码器及物理约束损失函数提升分割精度与鲁棒性。

AUNet: Attention-guided dense-upsampling networks for breast mass segmentation in whole mammograms

Mammography is one of the most commonly applied tools for early breast cancer screening. Automatic segmentation of breast masses in mammograms is essential but challenging due to the low signal-to-noise ratio and the wide variety of mass shapes and sizes. Existing methods deal with these challenges mainly by extracting mass-centered image patches manually or automatically. However, manual patch extraction is time-consuming and automatic patch extraction brings errors that could not be compensated in the following segmentation step. In this study, we propose a novel attention-guided dense-upsam…

Attention Model Enhanced Network for Classification of Breast Cancer Image

Breast cancer classification remains a challenging task due to inter-class ambiguity and intra-class variability. Existing deep learning-based methods try to confront this challenge by utilizing complex nonlinear projections. However, these methods typically extract global features from entire images, neglecting the fact that the subtle detail information can be crucial in extracting discriminative features. In this study, we propose a novel method named Attention Model Enhanced Network (AMEN), which is formulated in a multi-branch fashion with pixel-wised attention model and classification su…

基于二维卷积神经网络的乳腺癌诊断分类

Classification with 2-D Convolutional Neural Networks for breast cancer diagnosis

针对临床一维数据无法直接用于CNN的问题，提出将一维数据转换为二维图像的新预处理方法，在WBC和WDBC数据集上验证，WDBC上优于现有方法。

CLC-YOLO：一种用于实时泄漏感知乳腺超声病变分割的紧凑通道门控原型网络

CLC-YOLO: A Compact Channel-Gated Prototype Network for Real-Time Leakage-Aware Breast Ultrasound Lesion Segmentation

提出CLC-YOLO，通过通道门控局部高通残差改进YOLO26分割头，在四个乳腺超声数据集上提升组宏Dice，且开销极小。

Breast Mass Classification from Mammograms using Deep Convolutional Neural Networks

Mammography is the most widely used method to screen breast cancer. Because of its mostly manual nature, variability in mass appearance, and low signal-to-noise ratio, a significant number of breast masses are missed or misdiagnosed. In this work, we present how Convolutional Neural Networks can be used to directly classify pre-segmented breast masses in mammograms as benign or malignant, using a combination of transfer learning, careful pre-processing and data augmentation to overcome limited training data. We achieve state-of-the-art results on the DDSM dataset, surpassing human performance,…

Research on the Detection Method of Breast Cancer Deep Convolutional Neural Network Based on Computer Aid

Traditional breast cancer image classification methods require manual extraction of features from medical images, which not only require professional medical knowledge, but also have problems such as time-consuming and labor-intensive and difficulty in extracting high-quality features. Therefore, the paper proposes a computer-based feature fusion Convolutional neural network breast cancer image classification and detection method. The paper pre-trains two convolutional neural networks with different structures, and then uses the convolutional neural network to automatically extract the charact…

Boosted EfficientNet: Detection of Lymph Node Metastases in Breast Cancer Using Convolutional Neural Network

In recent years, advances in the development of whole-slide images have laid a foundation for the utilization of digital images in pathology. With the assistance of computer images analysis that automatically identifies tissue or cell types, they have greatly improved the histopathologic interpretation and diagnosis accuracy. In this paper, the Convolutional Neutral Network (CNN) has been adapted to predict and classify lymph node metastasis in breast cancer. Unlike traditional image cropping methods that are only suitable for large resolution images, we propose a novel data augmentation metho…

Classification of breast cancer histology images using transfer learning

Breast cancer is one of the leading causes of mortality in women. Early detection and treatment are imperative for improving survival rates, which have steadily increased in recent years as a result of more sophisticated computer-aided-diagnosis (CAD) systems. A critical component of breast cancer diagnosis relies on histopathology, a laborious and highly subjective process. Consequently, CAD systems are essential to reduce inter-rater variability and supplement the analyses conducted by specialists. In this paper, a transfer-learning based approach is proposed, for the task of breast histolog…

Conditional Generative Adversarial and Convolutional Networks for X-ray Breast Mass Segmentation and Shape Classification

This paper proposes a novel approach based on conditional Generative Adversarial Networks (cGAN) for breast mass segmentation in mammography. We hypothesized that the cGAN structure is well-suited to accurately outline the mass area, especially when the training data is limited. The generative network learns intrinsic features of tumors while the adversarial network enforces segmentations to be similar to the ground truth. Experiments performed on dozens of malignant tumors extracted from the public DDSM dataset and from our in-house private dataset confirm our hypothesis with very high Dice c…

通过关键帧特征中心引导提升乳腺超声视频分类性能

Boosting Breast Ultrasound Video Classification by the Guidance of Keyframe Feature Centers

针对乳腺超声视频数据集采集难的问题，提出KGA-Net和一致性损失，借助静态图像数据集的特征中心引导视频模型帧注意力，大幅提升BUSV数据集分类性能且具备可解释性。

EMT-NET: Efficient multitask network for computer-aided diagnosis of breast cancer

Deep learning-based computer-aided diagnosis has achieved unprecedented performance in breast cancer detection. However, most approaches are computationally intensive, which impedes their broader dissemination in real-world applications. In this work, we propose an efficient and light-weighted multitask learning architecture to classify and segment breast tumors simultaneously. We incorporate a segmentation task into a tumor classification network, which makes the backbone network learn representations focused on tumor regions. Moreover, we propose a new numerically stable loss function that e…

Automatic breast cancer grading in lymph nodes using a deep neural network

The progression of breast cancer can be quantified in lymph node whole-slide images (WSIs). We describe a novel method for effectively performing classification of whole-slide images and patient level breast cancer grading. Our method utilises a deep neural network. The method performs classification on small patches and uses model averaging for boosting. In the first step, region of interest patches are determined and cropped automatically by color thresholding and then classified by the deep neural network. The classification results are used to determine a slide level class and for further …

Response monitoring of breast cancer on DCE-MRI using convolutional neural network-generated seed points and constrained volume growing

Response of breast cancer to neoadjuvant chemotherapy (NAC) can be monitored using the change in visible tumor on magnetic resonance imaging (MRI). In our current workflow, seed points are manually placed in areas of enhancement likely to contain cancer. A constrained volume growing method uses these manually placed seed points as input and generates a tumor segmentation. This method is rigorously validated using complete pathological embedding. In this study, we propose to exploit deep learning for fast and automatic seed point detection, replacing manual seed point placement in our existing …

Segmentation of Infrared Breast Images Using MultiResUnet Neural Network

Breast cancer is the second leading cause of death for women in the U.S. Early detection of breast cancer is key to higher survival rates of breast cancer patients. We are investigating infrared (IR) thermography as a noninvasive adjunct to mammography for breast cancer screening. IR imaging is radiation-free, pain-free, and non-contact. Automatic segmentation of the breast area from the acquired full-size breast IR images will help limit the area for tumor search, as well as reduce the time and effort costs of manual segmentation. Autoencoder-like convolutional and deconvolutional neural netw…

MS-GWNN:multi-scale graph wavelet neural network for breast cancer diagnosis

Breast cancer is one of the most common cancers in women worldwide, and early detection can significantly reduce the mortality rate of breast cancer. It is crucial to take multi-scale information of tissue structure into account in the detection of breast cancer. And thus, it is the key to design an accurate computer-aided detection (CAD) system to capture multi-scale contextual features in a cancerous tissue. In this work, we present a novel graph convolutional neural network for histopathological image classification of breast cancer. The new method, named multi-scale graph wavelet neural ne…

Breast Ultrasound Computer-Aided Diagnosis Using Structure-Aware Triplet Path Networks

Breast ultrasound (US) is an effective imaging modality for breast cancer detec-tion and diagnosis. The structural characteristics of breast lesion play an im-portant role in Computer-Aided Diagnosis (CAD). In this paper, a novel struc-ture-aware triplet path networks (SATPN) was designed to integrate classifica-tion and two image reconstruction tasks to achieve accurate diagnosis on US im-ages with small training dataset. Specifically, we enhance clinically-approved breast lesion structure characteristics though converting original breast US imag-es to BIRADS-oriented feature maps (BFMs) with…

A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks

Breast cancer is one of the most common and deadliest cancers among women. Since histopathological images contain sufficient phenotypic information, they play an indispensable role in the diagnosis and treatment of breast cancers. To improve the accuracy and objectivity of Breast Histopathological Image Analysis (BHIA), Artificial Neural Network (ANN) approaches are widely used in the segmentation and classification tasks of breast histopathological images. In this review, we present a comprehensive overview of the BHIA techniques based on ANNs. First of all, we categorize the BHIA systems int…

利用视觉Transformer与图神经网络增强乳腺癌检测

Enhancing Breast Cancer Detection with Vision Transformers and Graph Neural Networks

本文提出融合视觉Transformer与图神经网络的乳腺癌检测框架，在CBIS-DDSM数据集上达到84.2%准确率，并通过可解释注意力热图辅助临床诊断。

Global Guidance Network for Breast Lesion Segmentation in Ultrasound Images

Automatic breast lesion segmentation in ultrasound helps to diagnose breast cancer, which is one of the dreadful diseases that affect women globally. Segmenting breast regions accurately from ultrasound image is a challenging task due to the inherent speckle artifacts, blurry breast lesion boundaries, and inhomogeneous intensity distributions inside the breast lesion regions. Recently, convolutional neural networks (CNNs) have demonstrated remarkable results in medical image segmentation tasks. However, the convolutional operations in a CNN often focus on local regions, which suffer from limit…

Breast mass segmentation based on ultrasonic entropy maps and attention gated U-Net

We propose a novel deep learning based approach to breast mass segmentation in ultrasound (US) imaging. In comparison to commonly applied segmentation methods, which use US images, our approach is based on quantitative entropy parametric maps. To segment the breast masses we utilized an attention gated U-Net convolutional neural network. US images and entropy maps were generated based on raw US signals collected from 269 breast masses. The segmentation networks were developed separately using US image and entropy maps, and evaluated on a test set of 81 breast masses. The attention U-Net traine…

Convolutional neural network based on transfer learning for breast cancer screening

Breast cancer is the most common cancer in the world and the most prevalent cause of death among women worldwide. Nevertheless, it is also one of the most treatable malignancies if detected early. In this paper, a deep convolutional neural network-based algorithm is proposed to aid in accurately identifying breast cancer from ultrasonic images. In this algorithm, several neural networks are fused in a parallel architecture to perform the classification process and the voting criteria are applied in the final classification decision between the candidate object classes where the output of each …

Multimodal Breast Lesion Classification Using Cross-Attention Deep Networks

Accurate breast lesion risk estimation can significantly reduce unnecessary biopsies and help doctors decide optimal treatment plans. Most existing computer-aided systems rely solely on mammogram features to classify breast lesions. While this approach is convenient, it does not fully exploit useful information in clinical reports to achieve the optimal performance. Would clinical features significantly improve breast lesion classification compared to using mammograms alone? How to handle missing clinical information caused by variation in medical practice? What is the best way to combine mamm…

Classification of Breast Cancer Lesions in Ultrasound Images by using Attention Layer and loss Ensembles in Deep Convolutional Neural Networks

Reliable classification of benign and malignant lesions in breast ultrasound images can provide an effective and relatively low cost method for early diagnosis of breast cancer. The accuracy of the diagnosis is however highly dependent on the quality of the ultrasound systems and the experience of the users (radiologists). The leverage in deep convolutional neural network approaches provided solutions in efficient analysis of breast ultrasound images. In this study, we proposed a new framework for classification of breast cancer lesions by use of an attention module in modified VGG16 architect…

Stan: Small tumor-aware network for breast ultrasound image segmentation

Breast tumor segmentation provides accurate tumor boundary, and serves as a key step toward further cancer quantification. Although deep learning-based approaches have been proposed and achieved promising results, existing approaches have difficulty in detecting small breast tumors. The capacity to detecting small tumors is particularly important in finding early stage cancers using computer-aided diagnosis (CAD) systems. In this paper, we propose a novel deep learning architecture called Small Tumor-Aware Network (STAN), to improve the performance of segmenting tumors with different size. The…

convoHER2: A Deep Neural Network for Multi-Stage Classification of HER2 Breast Cancer

Generally, human epidermal growth factor 2 (HER2) breast cancer is more aggressive than other kinds of breast cancer. Currently, HER2 breast cancer is detected using expensive medical tests are most expensive. Therefore, the aim of this study was to develop a computational model named convoHER2 for detecting HER2 breast cancer with image data using convolution neural network (CNN). Hematoxylin and eosin (H&E) and immunohistochemical (IHC) stained images has been used as raw data from the Bayesian information criterion (BIC) benchmark dataset. This dataset consists of 4873 images of H&E and IHC…

一种用于超声视频中乳腺病变分割的时空渐进融合网络

A Spatial-Temporal Progressive Fusion Network for Breast Lesion Segmentation in Ultrasound Videos

提出时空渐进融合网络STPFNet，结合统一架构、多尺度特征融合与前一帧先验，并构建UVBLS200数据集，实现超声视频乳腺病变分割性能超越现有方法。

A Novel method for IDC Prediction in Breast Cancer Histopathology images using Deep Residual Neural Networks

Invasive ductal carcinoma (IDC), which is also sometimes known as the infiltrating ductal carcinoma, is the most regular form of breast cancer. It accounts for about 80% of all breast cancers. According to the American Cancer Society, more than 180,000 women in the United States are diagnosed with invasive breast cancer each year. The survival rate associated with this form of cancer is about 77% to 93% depending on the stage at which they are being diagnosed. The invasiveness and the frequency of the occurrence of these disease makes it one of the difficult cancers to be diagnosed. Our propos…

Detection and Classification of Breast Cancer Metastates Based on U-Net

This paper presents U-net based breast cancer metastases detection and classification in lymph nodes, as well as patient-level classification based on metastases detection. The whole pipeline can be divided into five steps: preprocessing and data argumentation, patch-based segmentation, post processing, slide-level classification, and patient-level classification. In order to reduce overfitting and speedup convergence, we applied batch normalization and dropout into U-Net. The final Kappa score reaches 0.902 on training data.

BAGNet: Bidirectional Aware Guidance Network for Malignant Breast lesions Segmentation

Breast lesions segmentation is an important step of computer-aided diagnosis system, and it has attracted much attention. However, accurate segmentation of malignant breast lesions is a challenging task due to the effects of heterogeneous structure and similar intensity distributions. In this paper, a novel bidirectional aware guidance network (BAGNet) is proposed to segment the malignant lesion from breast ultrasound images. Specifically, the bidirectional aware guidance network is used to capture the context between global (low-level) and local (high-level) features from the input coarse sal…

AAU-net：一种用于超声图像乳腺病灶分割的自适应注意力U-net

AAU-net: An Adaptive Attention U-net for Breast Lesions Segmentation in Ultrasound Images

本文提出AAU-net，通过引入由通道和空间自注意力组成的混合自适应注意力模块替代传统卷积，以应对超声图像乳腺病灶分割中强度相似、形态多变和边界模糊等挑战，并在多个公开数据集上验证了其更优的分割性能和泛化能力。

Deep Multi-Magnification Networks for Multi-Class Breast Cancer Image Segmentation

Pathologic analysis of surgical excision specimens for breast carcinoma is important to evaluate the completeness of surgical excision and has implications for future treatment. This analysis is performed manually by pathologists reviewing histologic slides prepared from formalin-fixed tissue. In this paper, we present Deep Multi-Magnification Network trained by partial annotation for automated multi-class tissue segmentation by a set of patches from multiple magnifications in digitized whole slide images. Our proposed architecture with multi-encoder, multi-decoder, and multi-concatenation out…

Breast density classification with deep convolutional neural networks

Breast density classification is an essential part of breast cancer screening. Although a lot of prior work considered this problem as a task for learning algorithms, to our knowledge, all of them used small and not clinically realistic data both for training and evaluation of their models. In this work, we explore the limits of this task with a data set coming from over 200,000 breast cancer screening exams. We use this data to train and evaluate a strong convolutional neural network classifier. In a reader study, we find that our model can perform this task comparably to a human expert.