Research progress of deep learning-based object detection of optical remote sensing image by Yurong LIAO, Haining WANG, Cunbao LIN, Yang LI, Yuqiang FANG, Shuyan NI

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Ultrasound super resolution imaging for accurate uterus tumor detection and malignancy prediction by Ashwini Sawant, Sujata Kulkarni, Milind Sawant

“…A comparative analysis of copious relevant image de-speckling, image enhancement, segmentation, and feature extraction methods are carried out. …”
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Corticosteroid treatment prediction using chest X-ray and clinical data by Anzhelika Mezina, Samuel Genzor, Radim Burget, Vojtech Myska, Jan Mizera, Aleksandr Ometov

“…Moreover, we have proposed a unique methodology that combines machine learning and deep learning models based on Vision Transformer (ViT) and InceptionNet, preprocessing techniques, and pretraining strategies to deal with the specific characteristics of our data. Results: The experiments have proved that combining clinical data with CXR images achieves 8% higher accuracy than independent analysis of CXR images. …”
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Deep learning analysis for rheumatologic imaging: current trends, future directions, and the role of human by Jucheol Moon, Pratik Jadhav, Sangtae Choi

“…Recently, deep learning (DL), a subset of artificial intelligence, has emerged as a promising tool for enhancing medical imaging analysis. Convolutional neural networks, a DL model type, have shown great potential in medical image classification, segmentation, and anomaly detection, often surpassing human performance in tasks like tumor identification and disease severity grading. …”
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Recent Advances in Deep Learning-Based Spatiotemporal Fusion Methods for Remote Sensing Images by Zilong Lian, Yulin Zhan, Wenhao Zhang, Zhangjie Wang, Wenbo Liu, Xuhan Huang

“…Remote sensing images captured by satellites play a critical role in Earth observation (EO). …”
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Latent space autoencoder generative adversarial model for retinal image synthesis and vessel segmentation by K. Radha, Yepuganti Karuna

“…The results indicated that the synthetic data offered excellent segmentation performance, a crucial aspect in medical image analysis, where smaller datasets are often common. …”
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Development of a Transfer Learning-Based, Multimodal Neural Network for Identifying Malignant Dermatological Lesions From Smartphone Images by Jiawen Deng, Eddie Guo, Heather Jianbo Zhao, Kaden Venugopal, Myron Moskalyk

“…Methods: We used the PAD-UFES-20 dataset, which included 2298 sets of lesion images. Three neural network models were developed: (1) a clinical data-based network, (2) an image-based network using a pre-trained DenseNet-121 and (3) a multimodal network combining clinical and image data. …”
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STANet: A Novel Spatio-Temporal Aggregation Network for Depression Classification with Small and Unbalanced FMRI Data by Wei Zhang, Weiming Zeng, Hongyu Chen, Jie Liu, Hongjie Yan, Kaile Zhang, Ran Tao, Wai Ting Siok, Nizhuan Wang

“…STANet comprises the following steps: (1) Aggregate spatio-temporal information via independent component analysis (ICA). (2) Utilize multi-scale deep convolution to capture detailed features. (3) Balance data using the synthetic minority over-sampling technique (SMOTE) to generate new samples for minority classes. (4) Employ the attention-Fourier gate recurrent unit (AFGRU) classifier to capture long-term dependencies, with an adaptive weight assignment mechanism to enhance model generalization. …”
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Computer-aided diagnosis of Haematologic disorders detection based on spatial feature learning networks using blood cell images by Jamal Alsamri, Hamed Alqahtani, Ali M. Al-Sharafi, Abdulbasit A. Darem, Khalid Nazim, Abdul Sattar, Menwa Alshammeri, Ahmad A. Alzahrani, Marwa Obayya

“…This study presents a novel Computer-Aided Diagnosis of Haematologic Disorders Detection Based on Spatial Feature Learning Networks with Hybrid Model (CADHDD-SFLNHM) approach using Blood Cell Images. …”
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Machine learning for predicting Plasmodium liver stage development in vitro using microscopy imaging by Corin F. Otesteanu, Reto Caldelari, Volker Heussler, Raphael Sznitman

“…This study focuses on the liver stage development of the model organism Plasmodium berghei, employing fluorescent microscopy imaging and convolutional neural networks (CNNs) for analysis. …”
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TCCFNet: a semantic segmentation method for mangrove remote sensing images based on two-channel cross-fusion networks by Lixiang Fu, Yaoru Wang, Shulei Wu, Jiasen Zhuang, Zhongqiang Wu, Jian Wu, Huandong Chen, Yukai Chen

“…Deep learning techniques, particularly those based on CNNs and Transformers, have demonstrated significant progress in remote sensing image analysis. This study proposes TCCFNet (Two-Channel Cross-Fusion Network) to enhance the accuracy and robustness of mangrove remote sensing image semantic segmentation. …”
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Feature extraction and classification of digital rock images via pre-trained convolutional neural network and unsupervised machine learning by Masashige Shiga, Masao Sorai, Tetsuya Morishita, Masaatsu Aichi, Naoki Nishiyama, Takashi Fujii

“…To address this challenge, this study presents a novel approach for the classification and visualization of rock microstructure from micro-computed tomography images, leveraging pre-trained convolutional neural network (CNN) models (AlexNet, GoogLeNet, Inception v3 Net, ResNet, and DenseNet) combined with unsupervised machine learning (USML) techniques principal component analysis, multidimensional scaling, isometric mapping, t-distributed stochastic neighbor embedding (t-SNE), and uniform manifold approximation projection (UMAP)). …”
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Robust lung segmentation in Chest X-ray images using modified U-Net with deeper network and residual blocks by Wiley Tam, Paul Babyn, Javad Alirezaie

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Deep Learning Techniques in DICOM Files Classification: A Systematic Review by Mabirizi, Vicent, Kawuma, Simon, Natumanya, Deborah, Wasswa, William

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Novel Deep Learning-Based Facial Forgery Detection for Effective Biometric Recognition by Hansoo Kim

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Application of a Dual-Stream Network Collaboratively Based on Wavelet and Spatial-Channel Convolution in the Inpainting of Blank Strips in Marine Electrical Imaging Logging Images:... by Guilan Lin, Sinan Fang, Manxin Li, Hongtao Wu, Chenxi Xue, Zeyu Zhang

“…A dual-stream encoder–decoder network architecture is adopted, and the wavelet transform convolution (WTConv) module is utilized to enhance the multi-scale perception ability of the generator, achieving a collaborative analysis of the low-frequency formation structure and high-frequency fracture details. …”
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Introducing SPINE: A Holistic Approach to Synthetic Pulmonary Imaging Evaluation Through End-to-End Data and Model Management by Nikolaos Ntampakis, Vasileios Argyriou, Konstantinos Diamantaras, Konstantinos Goulianas, Panagiotis Sarigiannidis, Ilias Siniosoglou

“…We employ SPINE (Synthetic Pulmonary Imaging Evaluation) framework, a threefold synthetic images evaluation method including expert domain assessment, statistical data analysis and adversarial evaluation. …”
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PCRFed: personalized federated learning with contrastive representation for non-independently and identically distributed medical image segmentation by Shengyuan Liu, Ruofan Zhang, Mengjie Fang, Hailin Li, Tianwang Xun, Zipei Wang, Wenting Shang, Jie Tian, Di Dong

“…Abstract Federated learning (FL) has shown great potential in addressing data privacy issues in medical image analysis. However, varying data distributions across different sites can create challenges in aggregating client models and achieving good global model performance. …”
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Efficient Method for Robust Backdoor Detection and Removal in Feature Space Using Clean Data by Donik Vrsnak, Marko Subasic, Sven Loncaric

“…The steady increase of proposed backdoor attacks on deep neural networks highlights the need for robust defense methods for their detection and removal. …”
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A Near-Infrared Imaging System for Robotic Venous Blood Collection by Zhikang Yang, Mao Shi, Yassine Gharbi, Qian Qi, Huan Shen, Gaojian Tao, Wu Xu, Wenqi Lyu, Aihong Ji

“…The success of this robotic approach is heavily dependent on the quality of vein imaging. In this paper, we develop a vein imaging device based on the simulation analysis of vein imaging parameters and propose a U-Net+ResNet18 neural network for vein image segmentation. …”
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