Affinity Uncertainty-based Hard Negative Mining in Graph Contrastive Learning

Hard negative mining has shown effective in enhancing self-supervised contrastive learning (CL) on diverse data types, including graph CL (GCL). The existing hardness-aware CL methods typically treat negative instances that are most similar to the anchor instance as hard negatives, which helps improve the CL performance, especially on image data. However, this approach often fails to identify the hard negatives but leads to many false negatives on graph data. This is mainly due to that the learned graph representations are not sufficiently discriminative due to oversmooth representations and/or non-independent and identically distributed (non-i.i.d.) issues in graph data. To tackle this problem, this article proposes a novel approach that builds a discriminative model on collective affinity information (i.e., two sets of pairwise affinities between the negative instances and the anchor instance) to mine hard negatives in GCL. In particular, the proposed approach evaluates how confident/uncertain the discriminative model is about the affinity of each negative instance to an anchor instance to determine its hardness weight relative to the anchor instance. This uncertainty information is then incorporated into the existing GCL loss functions via a weighting term to enhance their performance. The enhanced GCL is theoretically grounded that the resulting GCL loss is equivalent to a triplet loss with an adaptive margin being exponentially proportional to the learned uncertainty of each negative instance. Extensive experiments on ten graph datasets show that our approach does the following: 1) consistently enhances different state-of-the-art (SOTA) GCL methods in both graph and node classification tasks and 2) significantly improves their robustness against adversarial attacks. Code is available at https://github.com/mala-lab/AUGCL.Comment: Accepted to TNNL

Association between different types of preoperative anemia and tumor characteristics, systemic inflammation, and survival in colorectal cancer

Background Patients with colorectal cancer often have anemia and other symptoms after diagnosis, especially in patients with advanced colorectal cancer. This study explored the association between different types of preoperative anemia and tumor characteristics and inflammatory response in patients with colorectal cancer and to evaluate the prognosis of patients with different types of anemia before operation. Methods The clinical data of 95 patients with colorectal cancer treated in the Fourth Hospital of Hebei Medical University from February 2016 to January 2018 were retrospectively analyzed. According to the hemoglobin concentration (Hb), mean corpuscular volume (MCV), mean hemoglobin content (MCH) and mean hemoglobin concentration (MCHC), the patients were divided into the non-anemia group, normal cell anemia group, and small cell anemia group. The three groups’ general data, oncological characteristics, and mGPS scores were compared. The patients were followed up for five years, and the survival analysis was carried out. The cox proportional hazard regression model was used to analyze the prognostic factors of patients with colorectal cancer. Results The preoperative anemia rate of patients with colorectal cancer was 43.15% (41/95). There were significant differences in gender, weight loss, CA724, tumor location, tumor size, TNM stage, mGPS score, and positive expression rate of Ki-67 among different anemia groups. There was a significant difference in survival time among a non-anemia group, small cell anemia group, and normal cell anemia group (P < 0.05). Multivariate analysis showed that tumor size, TNM stage, distant metastasis, mGPS score, Ki-67 positive expression rate, and anemia type were independent risk factors affecting the prognosis of colorectal cancer patients (P < 0.05). Conclusion The oncological characteristics of colorectal cancer patients with different types of preoperative anemia are different. Preoperative anemia and systemic inflammatory status are independent risk factors for the prognosis of colorectal cancer patients

Analysis of influencing factors of no/low response to preoperative concurrent chemoradiotherapy in locally advanced rectal cancer.

The aim of this study is to investigate the influencing factors associated with no/low response to preoperative concurrent chemoradiotherapy (CCRT) for locally advanced rectal cancer (LARC) patients. A total of 79 patients were included in this prospective study. Fifteen factors that might affect the resistance to CCRT were included in this logistic regression analysis, these factors include the general clinical data of patients, the expression status of tumor stem cell marker CD44v6 and the volumetric imaging parameters of primary tumor lesions. We found that the no/low response status to preoperative CCRT was positively correlated with the real tumor volume (RTV), the total surface area of tumor (TSA), and CD44v6 expression, whereas negatively correlated with the tumor compactness (TC). According to the results of logistic regression analysis, two formulas that could predict whether or not no/low response to preoperative CCRT were established. The Area Under Curve (AUC) of the two formulas and those significant measurement data (RTV, TC, TSA) were 0.900, 0.858, 0.771, 0.754, 0.859, the sensitivity were 95.8%, 79.17%, 62.50%, 95.83%, 62.5%, the specificity were 70.9%, 74.55%, 83.64%,47.27%, 96.36%, the positive predictive values were 58.96%, 57.58%, 62.51%,44.23%, 88.23%, the negative predictive values were 97.48%, 89.13%, 83.64%, 96.29%, and 85.48%, respectively