A Novel Network for Choroidal Segmentation Based on Enhanced Boundary Information by Wenbo Huang, Chaofan Qu, Yang Yan

“…Ablation studies and results from multiple medical image datasets validate that BENet consistently delivers precise segmentation outcomes. …”
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Automated karyogram analysis for early detection of genetic and neurodegenerative disorders: a hybrid machine learning approach by Sumaira Tabassum, M. Jawad Khan, Javaid Iqbal, Asim Waris, M. Adeel Ijaz

“…Although the deep learning-based architecture has yielded state-of-the-art performance in medical image anomaly detection, it cannot be generalized well because of the lack of anomalous datasets. …”
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Deep learning on CT scans to predict checkpoint inhibitor treatment outcomes in advanced melanoma by Laurens S. Ter Maat, Rob A. J. De Mooij, Isabella A. J. Van Duin, Joost J. C. Verhoeff, Sjoerd G. Elias, Tim Leiner, Wouter A. C. van Amsterdam, Max F. Troenokarso, Eran R. A. N. Arntz, Franchette W. P. J. Van den Berkmortel, Marye J. Boers-Sonderen, Martijn F. Boomsma, Fons J. M. Van den Eertwegh, Jan Willem de Groot, Geke A. P. Hospers, Djura Piersma, Art Vreugdenhil, Hans M. Westgeest, Ellen Kapiteijn, Ardine A. De Wit, Willeke A. M. Blokx, Paul J. Van Diest, Pim A. De Jong, Josien P. W. Pluim, Karijn P. M. Suijkerbuijk, Mitko Veta

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Deep Learning-Based Fully Automatic Segmentation of the Paranasal Sinuses in Chronic Rhinosinusitis Patients Using Computed Tomographic Images by Yuhang Wang, Xiaolei Zhang, Weidong Du, Na Dai, Yi Lyv, Keying Wu, Yiyang Tian, Yuxin Jie, Yu Lin, Weipiao Kang

“…We chose a custom 3D nnU-Net v2 network model because of its excellent performance in the field of 3D medical image segmentation, especially in automated training and accurate segmentation of complex structures. …”
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Artificial Intelligence-Based Skin Lesion Analysis and Skin Cancer Detection by Momina Qureshi, Muhammad Athar Javed Sethi, Sayed Shahid Hussain

“…This article not only offers a critical assessment of current methods but also tackles problems and indicates future directions for future research in the field of medical image categorization. This research has implications that extend beyond skin cancer diagnosis; it impacts several therapeutic applications and provides a solid foundation for further advancements in the field. …”
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PLZero: placeholder based approach to generalized zero-shot learning for multi-label recognition in chest radiographs by Chengrong Yang, Qiwen Jin, Fei Du, Jing Guo, Yujue Zhou

“…Abstract By leveraging large-scale image-text paired data for pre-training, the model can efficiently learn the alignment between images and text, significantly advancing the development of zero-shot learning (ZSL) in the field of intelligent medical image analysis. However, the heterogeneity between cross-modalities, false negatives in image-text pairs, and domain shift phenomena pose challenges, making it difficult for existing methods to effectively learn the deep semantic relationships between images and text. …”
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Transfer learning for non-image data in clinical research: A scoping review. by Andreas Ebbehoj, Mette Østergaard Thunbo, Ole Emil Andersen, Michala Vilstrup Glindtvad, Adam Hulman

“…While transfer learning has garnered considerable attention in medical image analysis, its use for clinical non-image data is not well studied. …”
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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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Volume and distribution of white matter hyperintensities in rheumatoid arthritis and ulcerative colitis patients by Jennifer G. Cox, James H. Cole, Matthew J. Kempton, Steven C. R. Williams, Marius de Groot

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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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Chlamydia psittaci pneumonia - evolutionary aspects on chest CT by Na Yang, Zhengqiu Ou, Qian Sun, Junping Pan, Jing Wu, Chen Xue

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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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