Deep Learning Models to Predict Fatal Pneumonia Using Chest X-Ray Images by Satoshi Anai, Junko Hisasue, Yoichi Takaki, Naohiko Hara

“…Deep learning is an artificial intelligence (AI) technology that has been applied to the interpretation of medical images. This study investigated the feasibility of classifying fatal pneumonia based on CXR images using deep learning models on publicly available platforms. …”
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Universal conditional networks (UniCoN) for multi-age embryonic cartilage segmentation with Sparsely annotated data by Nishchal Sapkota, Yejia Zhang, Zihao Zhao, Maria Jose Gomez, Yuhan Hsi, Jordan A. Wilson, Kazuhiko Kawasaki, Greg Holmes, Meng Wu, Ethylin Wang Jabs, Joan T. Richtsmeier, Susan M. Motch Perrine, Danny Z. Chen

“…These results highlight the potential of our approach for developing robust, universal models capable of handling diverse datasets with limited annotated data, a key challenge in DL-based medical image analysis.…”
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Optical Coherence Tomography Noise Reduction Using Anisotropic Local Bivariate Gaussian Mixture Prior in 3D Complex Wavelet Domain by Hossein Rabbani, Milan Sonka, Michael D. Abramoff

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Presegmenter Cascaded Framework for Mammogram Mass Segmentation by Urvi Oza, Bakul Gohel, Pankaj Kumar, Parita Oza

“…The presegmenter cascade framework has the potential to improve segmentation performance and mitigate FNs when integrated with any medical image segmentation framework, irrespective of the choice of the model.…”
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Partial Volume Reduction by Interpolation with Reverse Diffusion

“…<p>Many medical images suffer from the partial volume effect where a boundary between two structures of interest falls in the midst of a voxel giving a signal value that is a mixture of the two. …”
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Brain Tumor Detection and Classification Using IFF-FLICM Segmentation and Optimized ELM Model by Suvashisa Dash, Mohammed Siddique, Satyasis Mishra, Demissie J. Gelmecha, Sunita Satapathy, Davinder Singh Rathee, Ram Sewak Singh

“…Brain tumor detection and classification have become challenging and time-consuming for domain-specific radiologists and pathologists in medical image analysis. So, automatic detection and classification are essential to reduce the time of diagnosis. …”
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Addressing Label Noise in Colorectal Cancer Classification Using Cross-Entropy Loss and pLOF Methods With Stacking-Ensemble Technique by Ishrat Zahan Tani, Kah Ong Michael Goh, Md Nazmul Islam, Md Tarek Aziz, S. M. Hasan Mahmud, Dip Nandi

“…However, label noise in medical images and the dependence on a single model can lead to suboptimal model performance, which could potentially hinder the development of a sophisticated automated solution. …”
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Deep learning-based skin lesion analysis using hybrid ResUNet++ and modified AlexNet-Random Forest for enhanced segmentation and classification. by Saleem Mustafa, Arfan Jaffar, Muhammad Rashid, Sheeraz Akram, Sohail Masood Bhatti

“…There is a high probability that ResUNet++, which is highly proficient at medical image segmentation, can produce better segmentation of lesions than the simpler models. …”
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Robot-assisted Neuroendoscopy: Surgeon’s Third Hand – a Proof of Concept Study by Karnam Murali, Rychen Jonathan, Guzman Raphael, Cattin Philippe C., Rauter Georg, Gerig Nicolas

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Efficacy of a Novel Bioactive Glass-Polymer Composite for Enamel Remineralization following Erosive Challenge by Farnoosh Fallahzadeh, Soolmaz Heidari, Farhood Najafi, Maryam Hajihasani, Nooshin Noshiri, Neda F. Nazari

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Automated Audit and Self-Correction Algorithm for Seg-Hallucination Using MeshCNN-Based On-Demand Generative AI by Sihwan Kim, Changmin Park, Gwanghyeon Jeon, Seohee Kim, Jong Hyo Kim

“…Recent advancements in deep learning have significantly improved medical image segmentation. However, the generalization performance and potential risks of data-driven models remain insufficiently validated. …”
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Understanding perception of the radiology community concerning virtual reality (VR) and augmented reality (AR) technology in radiology education by Suneet Paulson, Dwight Figueiredo, Sushant Matre

“…Abstract Background Radiology education is crucial in developing the fundamental skills and knowledge for effectively interpreting medical images, planning interventions, and providing high-quality patient care. …”
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Repeatability, reproducibility, and the effects of radiotherapy on radiomic features of lowfield MR-LINAC images of the prostate by Parker Anderson, Nesrin Dogan, John Chetley Ford, Kyle Padgett, Garrett Simpson, Radka Stoyanova, Matthew Charles Abramowitz, Alan Dal Pra, Rodrigo Delgadillo

“…Through radiomics, a quantitative analysis of medical images, it is possible to adapt treatment early on, which may prevent or mitigate future adverse events. …”
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FBATCNet: A Temporal Convolutional Network With Frequency Band Attention for Decoding Motor Imagery EEG by Shuaishuai Ma, Jidong Lv, Wenjie Li, Yan Liu, Ling Zou, Yakang Dai

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Deep learning-based algorithm for classifying high-resolution computed tomography features in coal workers’ pneumoconiosis by Hantian Dong, Biaokai Zhu, Xiaomei Kong, Xuesen Su, Ting Liu, Xinri Zhang

“…Methods All chest high-resolution computed tomography (HRCT) medical images presented in this work were obtained from 217 coal workers' pneumoconiosis (CWP) patients and dust-exposed workers. …”
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Comparative analysis of deep learning and radiomic signatures for overall survival prediction in recurrent high-grade glioma treated with immunotherapy by Qi Wan, Clifford Lindsay, Chenxi Zhang, Jisoo Kim, Xin Chen, Jing Li, Raymond Y. Huang, David A. Reardon, Geoffrey S. Young, Lei Qin

“…Abstract Background Radiomic analysis of quantitative features extracted from segmented medical images can be used for predictive modeling of prognosis in brain tumor patients. …”
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The Social Construction of Categorical Data: Mixed Methods Approach to Assessing Data Features in Publicly Available Datasets by Theresa Willem, Alessandro Wollek, Theodor Cheslerean-Boghiu, Martha Kenney, Alena Buyx

“…As a standard, categorical data, such as patients’ gender, socioeconomic status, or skin color, are used to train models in fusion with other data types, such as medical images and text-based medical information. However, the effects of including categorical data features for model training in such data-scarce areas are underexamined, particularly regarding models intended to serve individuals equitably in a diverse population. …”
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Application of Fractal Analysis in Detecting Trabecular Bone Changes in Periapical Radiograph of Patients with Periodontitis by Parisa Soltani, Sajad Sami, Jaber Yaghini, Ehsan Golkar, Francesco Riccitiello, Gianrico Spagnuolo

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Systematic Coronary Risk Evaluation 2 (SCORE2), arterial stiffness, and subclinical coronary atherosclerosis in a population-based study by Susanna Strömberg, Andreas Stomby, Jan Engvall, Carl Johan Östgren

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Transformers for Neuroimage Segmentation: Scoping Review by Maya Iratni, Amira Abdullah, Mariam Aldhaheri, Omar Elharrouss, Alaa Abd-alrazaq, Zahiriddin Rustamov, Nazar Zaki, Rafat Damseh

“…Transformers are a promising deep learning approach for automated medical image segmentation. ObjectiveThis scoping review will synthesize current literature and assess the use of various transformer models for neuroimaging segmentation. …”
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