hmp-munet-hybrid-deep-learning-framework-for-automated-skin-lesion

HMP-MUNet: A Hybrid Deep Learning Framework for Automated Skin Lesion Segmentation in Dermoscopic Images

Computer-aided diagnosis systems for cutaneous lesions face significant challenges in achieving both high diagnostic accuracy and computational ...

Shenyang University of Chemical Technology

As a kind of transportation in a smart city, urban public bicycles have been adopted by major cities and bear the heavy responsibility of the "last mile" of urban public transportation. At present, the main problem of the urban public bicycle system is that it is difficult for users to rent a bike during peak h, and real-time monitoring cannot be solved adequately. Therefore, predicting the demand for bicycles in a certain period and performing redistribution in advance is of great significance for solving the lag of bicycle system scheduling with the help of IoT. Based on the HOSVD-LSTM prediction model, a prediction model of urban public bicycles based on the hybrid model is proposed by transforming the source data (multiple time series) into a high-order tensor time series. Furthermore, it uses the tensor decomposition technology (HOSVD decomposition) to extract new features (kernel tenor) from higher-order tensors. At the same time, these kernel tenors are directly used to train tensor LSTM models to obtain new kernel tenors. The inverse tensor decomposition and high-dimensional, multidimensional, and tensor dimensionality reduction were introduced. The new kernel tenor obtains the predicted value of the source sequence. Then the bicycle rental amount is predicted.

Short-Term Rental Forecast of Urban Public Bicycle Based on the HOSVD-LSTM Model in Smart City.

The uncommon p.L747P mutation in epidermal growth factor receptor (EGFR) exon 19 reveals to alter the response to tyrosine kinase inhibitors (TKIs) in patients diagnosed with advanced non-small cell lung cancer (NSCLC). However, the underlying mechanism is still not clear. This study aimed to investigate the clinical outcomes, binding affinities, and modes of action of currently available EGFR TKIs towards p.L747P mutation.

The Lifted Veil of Uncommon EGFR Mutation p.L747P in Non-Small Cell Lung Cancer: Molecular Feature and Targeting Sensitivity to Tyrosine Kinase Inhibitors.

With the continuous integration of deep learning and the technique of molecular biology, target detection models must accurately detect the position of each cell in the image and classify it correctly. We present a model for the multi-scale feature fusion of the existing human cell image dataset based on Gaussian mixedly clustering. First, a novel feature extraction network for extracting preliminary features at picture multi scales was presented, which was based on a residual neural network with Instance Normalization and a Mish activation function. Second, the presented model adopts the idea of feature fusion and introduced a new type of feature fusion network to integrate feature graphs on different scales. Furthermore, a Gaussian hybrid clustering algorithm was proposed to cluster the hyperparameters. Based on the experimental results, the average accuracy of the proposed model in the human cell image dataset exceeds 0.96, which improves by 11.9% compared with the existing target detection methods in the same field. Experiments show that the proposed model had been adapted to datasets with uneven sample distribution, providing new ideas for research on medical images.

Automatic detection of three cell types in a microscope image based on deep learning.

During the COVID-19 pandemic, the accurate forecasting and profiling of the supply of fresh commodities in urban supermarket chains may help the city government make better economic decisions, support activities of daily living, and optimize transportation to support social governance. In urban supermarket chains, the large variety of fresh commodities and the short shelf life of fresh commodities lead to the poor performance of the traditional fresh commodity supply forecasting algorithm.

Supply forecasting and profiling of urban supermarket chains based on tensor quantization exponential regression for social governance.

The accurate identification and analysis of chemical structures in molecular images are prerequisites of artificial intelligence for drug discovery. It is important to efficiently and automatically convert molecular images into machine-readable representations. Therefore, in this paper, we propose an automated molecular optical image recognition model based on deep learning, called Image2InChI. Additionally, the proposed Image2InChI introduces a novel feature fusion network with attention to integrate image patch and InChI prediction. The improved SwinTransformer as an encoder and the Transformer Decoder as a decoder with patch embedding are applied to predict the image features for the corresponding InChI. The experimental results showed that the Image2InChI model achieves an accuracy of InChI (InChI acc) of 99.8%, a Morgan FP of 94.1%, an accuracy of maximum common structures (MCS acc) of 94.8%, and an accuracy of longest common subsequence (LCS acc) of 96.2%. The experiments demonstrated that the proposed Image2InChI model improves the accuracy and efficiency of molecular image recognition and provided a valuable reference about optical chemical structure recognition for InChI.

Image2InChI: Automated Molecular Optical Image Recognition.

As Organic Light-Emitting Diode (OLED) technology advances in applications such as high-end displays, medical devices, and VR/AR systems, the development of high-performance materials that improve energy efficiency and support environmental sustainability has become increasingly important. Traditional methods like Density Functional Theory (DFT) are accurate but computationally expensive, motivating the need for efficient prediction tools. This paper presents 3D-SAT-OLED, a deep learning model based on three-dimensional self-attention to predict key OLED performance parameters. The model integrates 3D molecular structures with a Gaussian kernel-based feature extractor (GPE) and spatial position encoding, while Dynamic Tanh (DyT) and Swish-Gated Linear Unit (SwiGLU) activations enhance stability and nonlinear modeling. Experiments show that 3D-SAT-OLED achieves state-of-the-art performance on QM9 and OLED QM properties data sets, significantly outperforming existing models across , MAE, and other metrics, while demonstrating strong generalization and predictive accuracy.

Article	Year
Short-Term Rental Forecast of Urban Public Bicycle Based on the HOSVD-LSTM Model in Smart City. Sensors (Basel, Switzerland)\| PubMed ID: 32485884	2020
The Lifted Veil of Uncommon EGFR Mutation p.L747P in Non-Small Cell Lung Cancer: Molecular Feature and Targeting Sensitivity to Tyrosine Kinase Inhibitors. Frontiers in oncology\| PubMed ID: 35223527	2022
Automatic detection of three cell types in a microscope image based on deep learning. Journal of biophotonics\| PubMed ID: 36054791	2022
Supply forecasting and profiling of urban supermarket chains based on tensor quantization exponential regression for social governance. PeerJ. Computer science\| PubMed ID: 36426261	2022
Image2InChI: Automated Molecular Optical Image Recognition. Journal of chemical information and modeling\| PubMed ID: 38359459	2024
Geometry-Driven Attention Model with 3D Molecular Features for Multi-Property Prediction of OLED Materials. Journal of chemical information and modeling\| PubMed ID: 41172256	2025

Dazhou Li

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