Adversarial Deep Structured Nets for Mass Segmentation from Mammograms

Mass segmentation provides effective morphological features which are important for mass diagnosis. In this work, we propose a novel end-to-end network for mammographic mass segmentation which employs a fully convolutional network (FCN) to model a potential function, followed by a CRF to perform structured learning. Because the mass distribution varies greatly with pixel position, the FCN is combined with a position priori. Further, we employ adversarial training to eliminate over-fitting due to the small sizes of mammogram datasets. Multi-scale FCN is employed to improve the segmentation performance. Experimental results on two public datasets, INbreast and DDSM-BCRP, demonstrate that our end-to-end network achieves better performance than state-of-the-art approaches. \footnote{https://github.com/wentaozhu/adversarial-deep-structural-networks.git}Comment: Accepted by ISBI2018. arXiv admin note: substantial text overlap with arXiv:1612.0597

Clasificación de tumores en cáncer de mama basado en redes neuronales de convolución

El cáncer de mama es una de las causas más frecuentes de mortalidad en las mujeres. Con la llegada de los sistemas inteligentes, la detección automática de tumores en mamografías se ha convertido en un gran reto y puede jugar un papel crucial para mejorar el diagnóstico médico. En este trabajo, se propone un sistema de diagnóstico asistido por ordenador basado en técnicas de Deep Learning, específicamente en redes neuronales de convolución (CNN). El sistema está dividido en dos partes: en primer lugar, se realiza un preprocesamiento sobre las mamografías extraídas de una base de datos pública; posteriormente, las CNNs extraen características de las imágenes preprocesadas para finalmente clasificarlas en función de los dos tipos de tumores existentes: benignos y malignos. Los resultados de este estudio muestran que el sistema tiene una precisión del 80% en clasificación de tumores.Breast cancer is one of the most frequent causes of mortality in women. With the arrival of the artificial intelligent, the automatic detection of tumors in mammograms has become a big challenge and can play a crucial role in improving medical diagnosis. In this work, a computer-aided diagnosis system based on Deep Learning techniques, specifically in Convolutional Neural Networks (CNN), is proposed. The system is divided into two parts: first, a preprocessing is performed on mammograms taken from a public database; then, the CNN extracts features of the preprocessed images to finally classify them accordingly to the type of tissue. The results of this study show that the system has an accuracy of 80% in the classification

WG1N5327 - Medical image database for lossless codec evaluation

This document presents the CAIMAN Project medical image database for JPEG commitee evaluation works purposes.This document describes the CAIMAN ANR project contribution on medical images to be considered for AIC. In order to evaluate the medical image codec, we have compiled a list of medical image (X-ray, CT, MRI) for the development and analysis of medical image compression scheme. All images can be found on a secure FTP server, and can be use freely for research purpose

Prototype of a low-cost 3D breast ultrasound imaging system

This work describes a setup of the new acquisition system for 3D ultrasound images (i.e. B-mode) for breast tomography. Since premature and precise breast lesions diagnoses turn out in treatment more efficient and save lives, we are looking for a more precise, less painful exams and dose reduction for the patient. Therefore, a low cost scanner mechanism was built aiming to accommodate breasts under water while patient is laid down on a bed in which a robotic arm guides the ultrasound probe to acquire 2D images. Then 3D image is reconstructed using the 2D images due to render the mammary volume searching for lesions. The low cost scanner was built using a regular ultrasound machine, linear probe and major controls made by an Arduino Uno. We compared the acquired phantom images with gold standard images for mammary tissues diagnostics, i.e. Computerized Tomography and Magnetic Resonance Images. This study was evaluated using a paraffin-gel and mineral oil control phantom. Results show that the provided module is convicting enough to be used in local hospital as the next step of this study