Yunpeng Cai

Juan He, Xiaoyan Wang, Long Chen et al.

Wound healing is a complex process involving changes in collagen fibers. Accurate monitoring of these changes is crucial for assessing the progress of wound healing and has significant implications for guiding clinical treatment strategies and drug screening. However, traditional quantitative analysis methods focus on spatial characteristics such as collagen fiber alignment and variance, lacking threshold standards to differentiate between different stages of wound healing. To address this issue, we propose an innovative approach based on deep learning to predict the progression of wound healing by analyzing collagen fiber features in histological images of wound tissue. Leveraging the unique learning capabilities of deep learning models, our approach captures the feature variations of collagen fibers in histological images from different categories and classifies them into various stages of wound healing. To overcome the limited availability of histological image data, we employ a transfer learning strategy. Specifically, we fine-tune a VGG16 model pretrained on the ImageNet dataset to adapt it to the classification task of histological images of wounds. Through this process, our model achieves 82% accuracy in classifying six stages of wound healing. Furthermore, to enhance the interpretability of the model, we employ a class activation mapping technique called LayerCAM. LayerCAM reveals the image regions on which the model relies when making predictions, providing transparency to the model's decision-making process. This visualization not only helps us understand how the model identifies and evaluates collagen fiber features but also enhances trust in the model's prediction results. To the best of our knowledge, our proposed model is the first deep learning-based classification model used for predicting wound healing stages.

Limai Jiang, Yunpeng Cai

Generative models capture the true distribution of data, yielding semantically rich representations. Denoising diffusion models (DDMs) exhibit superior generative capabilities, though efficient representation learning for them are lacking. In this work, we employ a multi-level denoising autoencoder framework to expand the representation capacity of DDMs, which introduces sequentially consistent Diffusion Transformers and an additional timestep-dependent encoder to acquire embedding representations on the denoising Markov chain through self-conditional diffusion learning. Intuitively, the encoder, conditioned on the entire diffusion process, compresses high-dimensional data into directional vectors in latent under different noise levels, facilitating the learning of image embeddings across all timesteps. To verify the semantic adequacy of embeddings generated through this approach, extensive experiments are conducted on various datasets, demonstrating that optimally learned embeddings by DDMs surpass state-of-the-art self-supervised representation learning methods in most cases, achieving remarkable discriminative semantic representation quality. Our work justifies that DDMs are not only suitable for generative tasks, but also potentially advantageous for general-purpose deep learning applications.

Chaojie Ji, Hongwei Chen, Ruxin Wang et al.

Clustering techniques attempt to group objects with similar properties into a cluster. Clustering the nodes of an attributed graph, in which each node is associated with a set of feature attributes, has attracted significant attention. Graph convolutional networks (GCNs) represent an effective approach for integrating the two complementary factors of node attributes and structural information for attributed graph clustering. However, oversmoothing of GCNs produces indistinguishable representations of nodes, such that the nodes in a graph tend to be grouped into fewer clusters, and poses a challenge due to the resulting performance drop. In this study, we propose a smoothness sensor for attributed graph clustering based on adaptive smoothness-transition graph convolutions, which senses the smoothness of a graph and adaptively terminates the current convolution once the smoothness is saturated to prevent oversmoothing. Furthermore, as an alternative to graph-level smoothness, a novel fine-gained node-wise level assessment of smoothness is proposed, in which smoothness is computed in accordance with the neighborhood conditions of a given node at a certain order of graph convolution. In addition, a self-supervision criterion is designed considering both the tightness within clusters and the separation between clusters to guide the whole neural network training process. Experiments show that the proposed methods significantly outperform 12 other state-of-the-art baselines in terms of three different metrics across four benchmark datasets. In addition, an extensive study reveals the reasons for their effectiveness and efficiency.

Chaojie Ji, Yijia Zheng, Ruxin Wang et al.

Molecular optimization, which transforms a given input molecule X into another Y with desirable properties, is essential in molecular drug discovery. The traditional translating approaches, generating the molecular graphs from scratch by adding some substructures piece by piece, prone to error because of the large set of candidate substructures in a large number of steps to the final target. In this study, we present a novel molecular optimization paradigm, Graph Polish, which changes molecular optimization from the traditional "two-language translating" task into a "single-language polishing" task. The key to this optimization paradigm is to find an optimization center subject to the conditions that the preserved areas around it ought to be maximized and thereafter the removed and added regions should be minimized. We then propose an effective and efficient learning framework T&S polish to capture the long-term dependencies in the optimization steps. The T component automatically identifies and annotates the optimization centers and the preservation, removal and addition of some parts of the molecule, and the S component learns these behaviors and applies these actions to a new molecule. Furthermore, the proposed paradigm can offer an intuitive interpretation for each molecular optimization result. Experiments with multiple optimization tasks are conducted on four benchmark datasets. The proposed T&S polish approach achieves significant advantage over the five state-of-the-art baseline methods on all the tasks. In addition, extensive studies are conducted to validate the effectiveness, explainability and time saving of the novel optimization paradigm.

Chaojie Ji, Ruxin Wang, Rongxiang Zhu et al.

Due to the cost of labeling nodes, classifying a node in a sparsely labeled graph while maintaining the prediction accuracy deserves attention. The key point is how the algorithm learns sufficient information from more neighbors with different hop distances. This study first proposes a hop-aware attention supervision mechanism for the node classification task. A simulated annealing learning strategy is then adopted to balance two learning tasks, node classification and the hop-aware attention coefficients, along the training timeline. Compared with state-of-the-art models, the experimental results proved the superior effectiveness of the proposed Hop-aware Supervision Graph Attention Networks (HopGAT) model. Especially, for the protein-protein interaction network, in a 40% labeled graph, the performance loss is only 3.9%, from 98.5% to 94.6%, compared to the fully labeled graph. Extensive experiments also demonstrate the effectiveness of supervised attention coefficient and learning strategies.