Medical image classification under class imbalance

Many medical image classification tasks have a severe class imbalance problem. That is images of target classes of interest, e.g., certain types of diseases, only appear in a very small portion of the entire dataset. These medical image classification tasks share two common issues. First, only a small labeled training set is available due to the expensive manual labeling by highly skilled medical experts. Second, there exists a high imbalance ratio between rare class and common class. The common class occupies a high percentage of the entire dataset and usually has a large sample variety, which makes it difficult to collect a good representative training set for the common class. Convolutional Neural Network (CNN) is currently a state-of-the-art method for image classification. CNN relies on a large training dataset to achieve high classification performance. However, manual labeling is costly and may not even be feasible, which limits CNN from offering high classification performance in practice. This dissertation addresses these two challenging issues with the ultimate goal to improve classification effectiveness and minimize manual labeling effort by the domain experts. The main contributions of dissertation are summarized as follows. 1) We propose a new real data augmentation method called Unified LF&SM that jointly learns feature representation and a similarity matrix for recommending unlabeled images for the domain experts to verify in order to quickly expand the small labeled training set. Real data augmentation utilizes realistic unlabeled samples rather than synthetic samples. The key of real data augmentation is how to design an effective strategy to select representative samples for certain classes quickly from a large realistic unlabeled dataset. 2) We investigate the effectiveness of six different data augmentation methods and perform a sensitivity study using training sets of different sizes, varieties, and similarities when compared with the test set. 3) We propose a Hierarchical and Unified Data Augmentation (HuDA) method to collect a large representative training dataset for the common class. HuDA incorporates a class hierarchy: class differences on the high level (between the rare class and the common class) and class differences on the low level (between sub-classes of the rare class or the common class). HuDA is capable of significantly reducing time-consuming manual effort while achieving quite similar classification effectiveness as manual selection. 4) We propose a similarity-based active deep learning framework (SAL), which is the first approach to deal with both a significant class imbalance and a small seed training set as far as we know. Broader Impact: Triplet-based real data augmentation methods utilize the similarity between samples to learn a better feature representation. These methods aim to guarantee that the computed similarity between two samples from the same class is always bigger than the computed similarity between two samples from two different classes. First, our sensitivity study on six different data augmentation methods shows that triplet-based real data augmentation methods always offer the largest improvement on both the recommendation accuracy and the classification performance. These real data augmentation methods are easily extendable to other medical image classification tasks. Our work provides useful insight into how to choose a good training image dataset for medical image classification tasks. Second, to the best of our knowledge, SAL is the first active deep learning framework that deals with a significant class imbalance. Our experiments show that SAL nearly obtains the upper bound classification performance by labeling only 5.6% and 7.5% of all images for the Endoscopy dataset and the Caltech-256 dataset, respectively. This finding confirms that SAL significantly reduces the experts’ manual labeling efforts while achieving near optimal classification performance. SAL works for multi-class image classification and is easily extendable to other medical image classification tasks as well

Study on failure warning of tool magazine and automatic tool changer

Tool magazine and automatic tool changer (ATC) is used to store and change tools in a machining center, which plays an important role in automatic manufacturing. Therefore, the stability and reliability of tool magazine and ATC is very important to a machining center. Failures of tool magazine and ATC would increase ramp-up repair time and repair cost. So early warning system of failures for tool magazine and ATC becomes a research hotspot. The main vibration signals of tool magazine and ATC would occur obviously when the tool arm grasps a tool holder, draws a tool holder out of a tool into spindle or tool pocket and inserts a tool holder into spindle or tool pocket. To predict failures of tool magazine and ATC and improve the availability of machining center, a vibration test procedure and calculation method of vibration signal threshold of pull nails looseness which can lead to tool falling failures are proposed based on the vibration detection theory. Then, the vibration signals of tool changing are analyzed and the relationship between the maximum amplitude of vibration signals and the looseness severity of pull nails is also illustrated. The final experiment results show that the tool falling failure warning method is feasible to reduce the failures of tool magazine and ATC through the early warning system based on the threshold of vibration signals

Finite Element Dynamic Analysis on Residual Stress Distribution of Titanium Alloy and Titanium Matrix Composite after Shot Peening Treatment

The residual stress distribution introduced by shot peening (SP) in the deformed surface layer of Ti-6Al-4V and (TiB+TiC)/Ti-6Al-4V were simulated and studied via the three-dimensional (3D) finite element dynamic analysis and the experimental validation. The program of ANSYS/LS-DYNA was utilized, and the 3D homogeneous and inhomogeneous models were set up. The homogeneous model was established for simulating SP process on Ti-6Al-4V. The influence of three important parameters, the shot balls’ size, shot velocity and coverage rate on residual stress distribution were investigated. Numerical simulation results showed that these parameters contributed different effects on SP treatment. Using a simplified method, an inhomogeneous model for simulating SP process on (TiB+TiC)/Ti-6Al-4V was set up. The max tensile and compressive residual stress (CRS) was +1155 and −1511 MPa, respectively. Based on this stress distribution, the beneficial effect of reinforcements was indicated during deformation, retarding the damage to the matrix and keeping the adverse tensile stresses in the reinforcements. In order to verify the results of simulation, the residual stress distribution along depth was measured by X-ray diffraction (XRD) method. The residual stress distribution by experiments was agreed with the simulated results, which verified the availability of 3D finite element dynamic analysis

Study on failure warning of tool magazine and automatic tool changer

Tool magazine and automatic tool changer (ATC) is used to store and change tools in a machining center, which plays an important role in automatic manufacturing. Therefore, the stability and reliability of tool magazine and ATC is very important to a machining center. Failures of tool magazine and ATC would increase ramp-up repair time and repair cost. So early warning system of failures for tool magazine and ATC becomes a research hotspot. The main vibration signals of tool magazine and ATC would occur obviously when the tool arm grasps a tool holder, draws a tool holder out of a tool into spindle or tool pocket and inserts a tool holder into spindle or tool pocket. To predict failures of tool magazine and ATC and improve the availability of machining center, a vibration test procedure and calculation method of vibration signal threshold of pull nails looseness which can lead to tool falling failures are proposed based on the vibration detection theory. Then, the vibration signals of tool changing are analyzed and the relationship between the maximum amplitude of vibration signals and the looseness severity of pull nails is also illustrated. The final experiment results show that the tool falling failure warning method is feasible to reduce the failures of tool magazine and ATC through the early warning system based on the threshold of vibration signals

Study on failure warning of tool magazine and automatic tool changer

Tool magazine and automatic tool changer (ATC) is used to store and change tools in a machining center, which plays an important role in automatic manufacturing. Therefore, the stability and reliability of tool magazine and ATC is very important to a machining center. Failures of tool magazine and ATC would increase ramp-up repair time and repair cost. So early warning system of failures for tool magazine and ATC becomes a research hotspot. The main vibration signals of tool magazine and ATC would occur obviously when the tool arm grasps a tool holder, draws a tool holder out of a tool into spindle or tool pocket and inserts a tool holder into spindle or tool pocket. To predict failures of tool magazine and ATC and improve the availability of machining center, a vibration test procedure and calculation method of vibration signal threshold of pull nails looseness which can lead to tool falling failures are proposed based on the vibration detection theory. Then, the vibration signals of tool changing are analyzed and the relationship between the maximum amplitude of vibration signals and the looseness severity of pull nails is also illustrated. The final experiment results show that the tool falling failure warning method is feasible to reduce the failures of tool magazine and ATC through the early warning system based on the threshold of vibration signals

Exploiting Multiple Embeddings for Chinese Named Entity Recognition

Identifying the named entities mentioned in text would enrich many semantic applications at the downstream level. However, due to the predominant usage of colloquial language in microblogs, the named entity recognition (NER) in Chinese microblogs experience significant performance deterioration, compared with performing NER in formal Chinese corpus. In this paper, we propose a simple yet effective neural framework to derive the character-level embeddings for NER in Chinese text, named ME-CNER. A character embedding is derived with rich semantic information harnessed at multiple granularities, ranging from radical, character to word levels. The experimental results demonstrate that the proposed approach achieves a large performance improvement on Weibo dataset and comparable performance on MSRA news dataset with lower computational cost against the existing state-of-the-art alternatives.Comment: accepted at CIKM 201

Inferential models: A framework for prior-free posterior probabilistic inference

Posterior probabilistic statistical inference without priors is an important but so far elusive goal. Fisher's fiducial inference, Dempster-Shafer theory of belief functions, and Bayesian inference with default priors are attempts to achieve this goal but, to date, none has given a completely satisfactory picture. This paper presents a new framework for probabilistic inference, based on inferential models (IMs), which not only provides data-dependent probabilistic measures of uncertainty about the unknown parameter, but does so with an automatic long-run frequency calibration property. The key to this new approach is the identification of an unobservable auxiliary variable associated with observable data and unknown parameter, and the prediction of this auxiliary variable with a random set before conditioning on data. Here we present a three-step IM construction, and prove a frequency-calibration property of the IM's belief function under mild conditions. A corresponding optimality theory is developed, which helps to resolve the non-uniqueness issue. Several examples are presented to illustrate this new approach.Comment: 29 pages with 3 figures. Main text is the same as the published version. Appendix B is an addition, not in the published version, that contains some corrections and extensions of two of the main theorem