Computer-Aided Diagnosis for Melanoma using Ontology and Deep Learning Approaches

The emergence of deep-learning algorithms provides great potential to enhance the prediction performance of computer-aided supporting diagnosis systems. Recent research efforts indicated that well-trained algorithms could achieve the accuracy level of experienced senior clinicians in the Dermatology field. However, the lack of interpretability and transparency hinders the algorithms’ utility in real-life. Physicians and patients require a certain level of interpretability for them to accept and trust the results. Another limitation of AI algorithms is the lack of consideration of other information related to the disease diagnosis, for example some typical dermoscopic features and diagnostic guidelines. Clinical guidelines for skin disease diagnosis are designed based on dermoscopic features. However, a structured and standard representation of the relevant knowledge in the skin disease domain is lacking. To address the above challenges, this dissertation builds an ontology capable of formally representing the knowledge of dermoscopic features and develops an explainable deep learning model able to diagnose skin diseases and dermoscopic features. Additionally, large-scale, unlabeled datasets can learn from the trained model and automate the feature generation process. The computer vision aided feature extraction algorithms are combined with the deep learning model to improve the overall classification accuracy and save manual annotation efforts

Identifying Patch Correctness in Test-Based Program Repair

Test-based automatic program repair has attracted a lot of attention in recent years. However, the test suites in practice are often too weak to guarantee correctness and existing approaches often generate a large number of incorrect patches. To reduce the number of incorrect patches generated, we propose a novel approach that heuristically determines the correctness of the generated patches. The core idea is to exploit the behavior similarity of test case executions. The passing tests on original and patched programs are likely to behave similarly while the failing tests on original and patched programs are likely to behave differently. Also, if two tests exhibit similar runtime behavior, the two tests are likely to have the same test results. Based on these observations, we generate new test inputs to enhance the test suites and use their behavior similarity to determine patch correctness. Our approach is evaluated on a dataset consisting of 139 patches generated from existing program repair systems including jGenProg, Nopol, jKali, ACS and HDRepair. Our approach successfully prevented 56.3\% of the incorrect patches to be generated, without blocking any correct patches.Comment: ICSE 201

A Near-Linear Time Sampler for the Ising Model

We give a near-linear time sampler for the Gibbs distribution of the ferromagnetic Ising models with edge activities $\boldsymbol{\beta} > 1$ and external fields $\boldsymbol{\lambda}<1$ (or symmetrically, $\boldsymbol{\lambda}>1$) on general graphs with bounded or unbounded maximum degree. Our algorithm is based on the field dynamics given in [CLV21]. We prove the correctness and efficiency of our algorithm by establishing spectral independence of distribution of the random cluster model and the rapid mixing of Glauber dynamics on the random cluster model in a low-temperature regime, which may be of independent interest

Unsupervised Abstractive Dialogue Summarization for Tete-a-Tetes

High-quality dialogue-summary paired data is expensive to produce and domain-sensitive, making abstractive dialogue summarization a challenging task. In this work, we propose the first unsupervised abstractive dialogue summarization model for tete-a-tetes (SuTaT). Unlike standard text summarization, a dialogue summarization method should consider the multi-speaker scenario where the speakers have different roles, goals, and language styles. In a tete-a-tete, such as a customer-agent conversation, SuTaT aims to summarize for each speaker by modeling the customer utterances and the agent utterances separately while retaining their correlations. SuTaT consists of a conditional generative module and two unsupervised summarization modules. The conditional generative module contains two encoders and two decoders in a variational autoencoder framework where the dependencies between two latent spaces are captured. With the same encoders and decoders, two unsupervised summarization modules equipped with sentence-level self-attention mechanisms generate summaries without using any annotations. Experimental results show that SuTaT is superior on unsupervised dialogue summarization for both automatic and human evaluations, and is capable of dialogue classification and single-turn conversation generation

Effect of low molecular weight heparin and ulinastatin as a combined therapy on soluble myeloid cell expression and intestinal mucosal function in patients with severe pancreatitis

Purpose: To investigate the effect of low molecular weight heparins (LMWHs) and ulinastatin on soluble myeloid cells and intestinal mucosal function (IMF) in patients with severe pancreatitis. Methods: A total of 107 patients with severe pancreatitis were divided into two groups: control group (CG, n = 53) and study group (SG, n = 54). The CG was treated with LMWH while SG was similarly treated but in addition received ulinastatin simultaneously. The following parameters were evaluated in the two groups: treatment effects, IMF, time for various indicators to normalize, vascular endothelial function, complication symptoms, T lymphoid subgroup indicators, inflammatory factors, anti-inflammatory factors, soluble B7-H2, and soluble myeloid cell receptor-1 level changes. Results: After treatment, SG showed lower levels of L/M value, DAO and D-lactic acid than in CG (p &lt; 0.05). Gastrointestinal function, leukocytes, amylase, and body temperature in SG had a shorter time to return to normal than in CG (p &lt; 0.05). The levels of IL-10 in SG were higher than in CG, while sB7-H2, TNF-α, sTREM-1 and IL-1 levels were lower than those in the CG (p &lt; 0.05). After treatment, NO levels in SG were higher, but TXB2, vWF and ET levels were lower than in CG (p &lt; 0.05). In addition, CD4+, CD4+/CD8+ indicators were higher and CD8+ lower in SG than in CG (p &lt; 0.05). Conclusion: Ulinastatin + LMWHs improves IMF in patients suffering from severe pancreatitis, shortens the time for various indicators to normalize, and reduces incidence of complications. However, further clinical trials are required to ascertain this therapeutic strategy for the management of severe pancreatitis. Keywords: Low molecular weight heparin; Ulinastatin; Severe pancreatitis; Soluble myeloid cell expression; Intestinal mucosal function; Treatment effec