Comparing Methods for segmentation of Microcalcification Clusters in Digitized Mammograms

Filtering for More Accurate Dense Tissue Segmentation in Digitized Mammograms

Breast tissue segmentation into dense and fat tissue is important for determining the breast density in mammograms. Knowing the breast density is important both in diagnostic and computer-aided detection applications. There are many different ways to express the density of a breast and good quality segmentation should provide the possibility to perform accurate classification no matter which classification rule is being used. Knowing the right breast density and having the knowledge of changes in the breast density could give a hint of a process which started to happen within a patient. Mammog…

Predicting invasive ductal carcinoma using a Reinforcement Sample Learning Strategy using Deep Learning

Invasive ductal carcinoma is a prevalent, potentially deadly disease associated with a high rate of morbidity and mortality. Its malignancy is the second leading cause of death from cancer in women. The mammogram is an extremely useful resource for mass detection and invasive ductal carcinoma diagnosis. We are proposing a method for Invasive ductal carcinoma that will use convolutional neural networks (CNN) on mammograms to assist radiologists in diagnosing the disease. Due to the varying image clarity and structure of certain mammograms, it is difficult to observe major cancer characteristics…

Understanding the robustness of deep neural network classifiers for breast cancer screening

Deep neural networks (DNNs) show promise in breast cancer screening, but their robustness to input perturbations must be better understood before they can be clinically implemented. There exists extensive literature on this subject in the context of natural images that can potentially be built upon. However, it cannot be assumed that conclusions about robustness will transfer from natural images to mammogram images, due to significant differences between the two image modalities. In order to determine whether conclusions will transfer, we measure the sensitivity of a radiologist-level screenin…

揭示深度学习乳腺癌风险模型中的乳腺X线表型

Revealing Mammographic Phenotypes in Deep Learning Breast Cancer Risk Models

通过聚类预训练模型Mirai的补丁嵌入，识别与五年癌症风险相关的重复性乳腺X线表型，发现风险增加的表型捕捉复杂结构（如致密组织、微钙化）和捷径伪影（如夹子），并与年龄和BI-RADS密度强相关。

Deep Learning to Improve Breast Cancer Early Detection on Screening Mammography

The rapid development of deep learning, a family of machine learning techniques, has spurred much interest in its application to medical imaging problems. Here, we develop a deep learning algorithm that can accurately detect breast cancer on screening mammograms using an "end-to-end" training approach that efficiently leverages training datasets with either complete clinical annotation or only the cancer status (label) of the whole image. In this approach, lesion annotations are required only in the initial training stage, and subsequent stages require only image-level labels, eliminating the …

一种从互补乳腺成像模态中学习统一表征的训练方案

A training regime to learn unified representations from complementary breast imaging modalities

针对DBT临床应用受限、DBT-FFDM联合采集增加检查负担的问题，提出利用两种模态互补诊断信号学习高层表征的机器学习方法，实验表明该表征的乳腺病变检测精度优于单模态模型。

Method and System for Image Analysis to Detect Cancer

Breast cancer is the most common cancer and is the leading cause of cancer death among women worldwide. Detection of breast cancer, while it is still small and confined to the breast, provides the best chance of effective treatment. Computer Aided Detection (CAD) systems that detect cancer from mammograms will help in reducing the human errors that lead to missing breast carcinoma. Literature is rich of scientific papers for methods of CAD design, yet with no complete system architecture to deploy those methods. On the other hand, commercial CADs are developed and deployed only to vendors' mam…