Related papers: Two-stage multi-scale breast mass segmentation for full mammogram analysis without user intervention

Two-stage multi-scale breast mass segmentation for full mammogram analysis without user intervention

URL: http://arxiv.org/abs/2002.12079v2
Date: Tue, 8 Dec 2020 09:50:41 GMT
Title: Two-stage multi-scale breast mass segmentation for full mammogram analysis without user intervention
Authors: Yutong Yan, Pierre-Henri Conze, Gwenol\'e Quellec, Mathieu Lamard, B\'eatrice Cochener, Gouenou Coatrieux
Abstract summary: Mammography is the primary imaging modality used for early detection and diagnosis of breast cancer. Among diverse types of breast abnormalities, masses are the most important clinical findings of breast carcinomas. We present a two-stage multi-scale pipeline that provides accurate mass contours from high-resolution full mammograms.
Score: 2.7490008316742096
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Mammography is the primary imaging modality used for early detection and diagnosis of breast cancer. X-ray mammogram analysis mainly refers to the localization of suspicious regions of interest followed by segmentation, towards further lesion classification into benign versus malignant. Among diverse types of breast abnormalities, masses are the most important clinical findings of breast carcinomas. However, manually segmenting breast masses from native mammograms is time-consuming and error-prone. Therefore, an integrated computer-aided diagnosis system is required to assist clinicians for automatic and precise breast mass delineation. In this work, we present a two-stage multi-scale pipeline that provides accurate mass contours from high-resolution full mammograms. First, we propose an extended deep detector integrating a multi-scale fusion strategy for automated mass localization. Second, a convolutional encoder-decoder network using nested and dense skip connections is employed to fine-delineate candidate masses. Unlike most previous studies based on segmentation from regions, our framework handles mass segmentation from native full mammograms without any user intervention. Trained on INbreast and DDSM-CBIS public datasets, the pipeline achieves an overall average Dice of 80.44% on INbreast test images, outperforming state-of-the-art. Our system shows promising accuracy as an automatic full-image mass segmentation system. Extensive experiments reveals robustness against the diversity of size, shape and appearance of breast masses, towards better interaction-free computer-aided diagnosis.

Related papers

Breast Ultrasound Report Generation using LangChain [58.07183284468881]
We propose the integration of multiple image analysis tools through a LangChain using Large Language Models (LLM) into the breast reporting process. Our method can accurately extract relevant features from ultrasound images, interpret them in a clinical context, and produce comprehensive and standardized reports.
arXiv Detail & Related papers (2023-12-05T00:28:26Z)
Multi-Head Feature Pyramid Networks for Breast Mass Detection [48.24995569980701]
We propose the multi-head feature pyramid module (MHFPN) to solve the problem of unbalanced focus of target boxes during feature map fusion. Experimental studies show that, comparing to the SOTA detection baselines, our method improves by 6.58% (in AP@50) and 5.4% (in TPR@50) on the commonly used INbreast dataset.
arXiv Detail & Related papers (2023-02-22T03:02:52Z)
High-resolution synthesis of high-density breast mammograms: Application to improved fairness in deep learning based mass detection [48.88813637974911]
Computer-aided detection systems based on deep learning have shown good performance in breast cancer detection. High-density breasts show poorer detection performance since dense tissues can mask or even simulate masses. This study aims to improve the mass detection performance in high-density breasts using synthetic high-density full-field digital mammograms.
arXiv Detail & Related papers (2022-09-20T15:57:12Z)
Act Like a Radiologist: Towards Reliable Multi-view Correspondence Reasoning for Mammogram Mass Detection [49.14070210387509]
We propose an Anatomy-aware Graph convolutional Network (AGN) for mammogram mass detection. AGN is tailored for mammogram mass detection and endows existing detection methods with multi-view reasoning ability. Experiments on two standard benchmarks reveal that AGN significantly exceeds the state-of-the-art performance.
arXiv Detail & Related papers (2021-05-21T06:48:34Z)
Global Guidance Network for Breast Lesion Segmentation in Ultrasound Images [84.03487786163781]
We develop a deep convolutional neural network equipped with a global guidance block (GGB) and breast lesion boundary detection modules. Our network outperforms other medical image segmentation methods and the recent semantic segmentation methods on breast ultrasound lesion segmentation.
arXiv Detail & Related papers (2021-04-05T13:15:22Z)
DenseNet for Breast Tumor Classification in Mammographic Images [0.0]
The aim of this study is to build a deep convolutional neural network method for automatic detection, segmentation, and classification of breast lesions in mammography images. Based on deep learning the Mask-CNN (RoIAlign) method was developed to features selection and extraction; and the classification was carried out by DenseNet architecture.
arXiv Detail & Related papers (2021-01-24T03:30:59Z)
ESTAN: Enhanced Small Tumor-Aware Network for Breast Ultrasound Image Segmentation [0.0]
We propose a novel deep neural network architecture, namely Enhanced Small Tumor-Aware Network (ESTAN) to accurately segment breast tumors. ESTAN introduces two encoders to extract and fuse image context information at different scales and utilizes row-column-wise kernels in the encoder to adapt to breast anatomy.
arXiv Detail & Related papers (2020-09-27T16:42:59Z)
On segmentation of pectoralis muscle in digital mammograms by means of deep learning [1.7114784273243784]
The present paper introduces a two-step segmentation strategy based on a combined use of data-driven prediction and graph-based image processing. The proposed method employs a convolutional neural network (CNN) which is designed to predict the location of breast-pectoral boundary. The results of comparative analysis show considerable improvement over state-of-the-art, while offering the possibility of model-free and fully automatic processing.
arXiv Detail & Related papers (2020-08-29T03:38:11Z)
Automatic elimination of the pectoral muscle in mammograms based on anatomical features [0.0]
Digital mammogram inspection is the most popular technique for early detection of abnormalities in human breast tissue. The presence of the pectoral muscle might affect the results of breast lesions detection. We propose an approach based on anatomical features to tackle this problem.
arXiv Detail & Related papers (2020-08-17T20:36:46Z)
A Two-Stage Multiple Instance Learning Framework for the Detection of Breast Cancer in Mammograms [13.842620686759616]
Mammograms are commonly employed in the large scale screening of breast cancer. We propose a two-stage Multiple Instance Learning framework for image-level detection of malignancy. A global image-level feature is computed as a weighted average of patch-level features learned using a CNN. Our method performed well on the task of localization of masses with an average Precision/Recall of 0.76/0.80 and acheived an average AUC of 0.91 on the imagelevel classification task.
arXiv Detail & Related papers (2020-04-24T13:06:47Z)
Stan: Small tumor-aware network for breast ultrasound image segmentation [68.8204255655161]
We propose a novel deep learning architecture called Small Tumor-Aware Network (STAN) to improve the performance of segmenting tumors with different size. The proposed approach outperformed the state-of-the-art approaches in segmenting small breast tumors.
arXiv Detail & Related papers (2020-02-03T22:25:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.