This work tackles the automatic fine-grained slide quality assessment problem for digitized direct smears test using the Gram staining protocol. Automatic quality assessment can provide useful information for the pathologists and the whole digital pathology workflow. For instance, if the system found a slide to have a low staining quality, it could send a request to the automatic slide preparation system to remake the slide. If the system detects severe damage in the slides, it could notify the experts that manual microscope reading may be required. In order to address the quality assessment problem, we propose a deep neural network based framework to automatically assess the slide quality in a semantic way. Specifically, the first step of our framework is to perform dense fine-grained region classification on the whole slide and calculate the region histogram of the label distributions. Next, our framework will generate assessments of the slide quality from various perspectives: staining quality, information density, damage level and which regions are more valuable for subsequent high-magnification analysis. To make the information more accessible, we present our results in the form of a heat map and text summaries. Additionally, in order to stimulate research in this direction, we propose a novel dataset for slide quality assessment. Experiments show that the proposed framework outperforms recent related works.
More recently, deep learning methods are also being investigated in several microscopic medical applications @cite_12 @cite_27 . However, these works address different problems thus their frameworks are not suitable for our proposed tasks. Perhaps the most similar work to us is @cite_22 where the authors use SVM to select the regions of interest in a coarse-grain style using hand-crafted feature descriptors. In contrast, we differ significantly: 1. We propose a novel fine-grained slide quality assessment problem with a new larger, improved, and more difficult dataset. 2. We propose a novel DNN based framework that is 20x faster in training, 4x faster in testing and more accurate compared to @cite_22 in the region classification task.
@cite_27 @cite_22 @cite_12
2533800772 2736827300 2950104591
This review covers computer-assisted analysis of images in the field of medical imaging. Recent advances in machine learning, especially with regard to deep learning, are helping to identify, classify, and quantify patterns in medical images. At the core of these advances is the ability to exploit hierarchical feature representations learned solely from data, instead of features designed by hand according to domain-specific knowledge. Deep learning is rapidly becoming the state of the art, leading to enhanced performance in various medical applications. We introduce the fundamentals of deep learning methods and review their successes in image registration, detection of anatomical and cellular structures, tissue segmentation, computer-aided disease diagnosis and prognosis, and so on. We conclude by discussing research issues and suggesting future directions for further improvement. Gram stained direct smears test is clinically useful in early identification of infections. Unfortunately, this practice is considered time consuming and labour intensive. Most existing effort in this area is to perform highmagnification analysis of images taken from manually selected areas. In this paper, we address the problem of the automatic selection of candidate areas (or patches) for subsequent high-magnification analysis. Specifically, we explore and study the possibility of selecting good working areas based on low-magnification images, where bacteria are likely to be found when viewed in high-magnification images. To this end, we develop an approach to classify the areas of interest according to the textural information of the image patch. We explore and study the efficacy of traditional textural features such as Histogram of Gradients, Local Binary Patterns, and 2 Dimensional Discrete Cosine Transform. Experiments show that the best variant method is able to select working areas where it is likely to find bacteria in high-powered objective images in a wide range of images. The ability to automatically learn task specific feature representations has led to a huge success of deep learning methods. When large training data is scarce, such as in medical imaging problems, transfer learning has been very effective. In this paper, we systematically investigate the process of transferring a Convolutional Neural Network, trained on ImageNet images to perform image classification, to kidney detection problem in ultrasound images. We study how the detection performance depends on the extent of transfer. We show that a transferred and tuned CNN can outperform a state-of-the-art feature engineered pipeline and a hybridization of these two techniques achieves 20 higher performance. We also investigate how the evolution of intermediate response images from our network. Finally, we compare these responses to state-of-the-art image processing filters in order to gain greater insight into how transfer learning is able to effectively manage widely varying imaging regimes.