Accurate segmentation of clustered microcalcifications in mammography is crucial for the diagnosis and treatment of breast cancer. Despite exhibiting expert-level accuracy, recent deep learning advancements in medical image segmentation provide insufficient contribution to practical applications, due to the domain shift resulting from differences in patient postures, individual gland density, and imaging modalities of mammography etc. In this paper, a novel framework named MLN-net, which can accurately segment multi-source images using only single source images, is proposed for clustered microcalcification segmentation. We first propose a source domain image augmentation method to generate multi-source images, leading to improved generalization. And a structure of multiple layer normalization (LN) layers is used to construct the segmentation network, which can be found efficient for clustered microcalcification segmentation in different domains. Additionally, a branch selection strategy is designed for measuring the similarity of the source domain data and the target domain data. To validate the proposed MLN-net, extensive analyses including ablation experiments are performed, comparison of 12 baseline methods. Extensive experiments validate the effectiveness of MLN-net in segmenting clustered microcalcifications from different domains and the its segmentation accuracy surpasses state-of-the-art methods. Code will be available at https://github.com/yezanting/MLN-NET-VERSON1.

We have previously presented a coarse-to-fine hierarchical pyra(cid:173) mid/neural network (HPNN) architecture which combines multi(cid:173) scale image processing techniques with neural networks. The first application is the detection of microcalcifications. The:oarse-to-fine HPNN was designed to learn large-scale context in(cid:173) formation for detecting small objects like microcalcifications. Re(cid:173) ceiver operating characteristic (ROC) analysis suggests that the hierarchical architecture improves detection performance of a well established CAD system by roughly 50 %. The second application is to detect mammographic masses directly. Since masses are large, extended objects, the coarse-to-fine HPNN architecture is not suit(cid:173) able for this problem.

Artificial Intelligence has made a great impact in the medical care system because of its powerful data analytics tools and filtration of valuable data from the unstructured pile of information. AI has a vital role to play in clinical decision-making and connecting patients with resources for self-management. In the healthcare industry, medical imaging brings in a great quantity of pixelated data taken from X-rays, CT scans, or MRIs. Using AI to analyze these high resolutions of imaging would help the radiologists and doctors to be more productive in less time and improve their accuracy. In the medical field, time is a valuable matter.

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 screening mammogram image classifier to four commonly studied input perturbations that natural image classifiers are sensitive to. We find that mammogram image classifiers are also sensitive to these perturbations, which suggests that we can build on the existing literature. We also perform a detailed analysis on the effects of low-pass filtering, and find that it degrades the visibility of clinically meaningful features called microcalcifications. Since low-pass filtering removes semantically meaningful information that is predictive of breast cancer, we argue that it is undesirable for mammogram image classifiers to be invariant to it. This is in contrast to natural images, where we do not want DNNs to be sensitive to low-pass filtering due to its tendency to remove information that is human-incomprehensible.

One big theme in 2017 in AI research was the idea of interpretability. How should AI systems explain their decisions to engender trust in their humans users? Can we trust a decision if we don't understand the factors that informed it? I'll have a lot more to say on the latter question some other time, which is philosophical rather than technical in nature, but today I wanted to share some of our research into the first question. Can our models explain their decisions in a way that can convince humans to trust them? I am a radiologist, which makes me something of an expert in the field of human image analysis. We are often asked to explain our assessment of an image, to our colleagues or other doctors or patients. In general, there are two things we express.

This blog has been talking a lot about Machine Learning (ML) with regard to tabular data. That makes sense because predictive algorithms based on tabular data are often easy to implement and have a lot of potential to improve outcomes. Also, we have access to a lot of tabular data from the EHR. However, ML is capable of doing a lot more than predicting probabilities on tabular data, and there are incredible opportunities in other areas of healthcare. One in particular is in Radiology and Pathology departments.

We have previously presented a coarse-to-fine hierarchical pyramid/neural network(HPNN) architecture which combines multiscale image processing techniques with neural networks.