In the field of medical image analysis, there is a substantial need for high-resolution (HR) images to improve diagnostic accuracy. However, it is a challenging task to obtain HR medical images, as it requires advanced instruments and significant time. Deep learning-based super-resolution methods can help to improve the resolution and perceptual quality of low-resolution (LR) medical images. Recently, Generative Adversarial Network (GAN) based methods have shown remarkable performance among deep learning-based super-resolution methods. Real-Enhanced Super-Resolution Generative Adversarial Network (Real-ESRGAN) is a practical model for recovering HR images from real-world LR images. In our proposed approach, we use transfer learning technique and fine-tune the pre-trained Real-ESRGAN model using medical image datasets. This technique helps in improving the performance of the model. We employ the high-order degradation model of the Real-ESRGAN which better simulates real-world image degradations. This adaptation allows for generating more realistic degraded medical images, resulting in improved performance. The focus of this paper is on enhancing the resolution and perceptual quality of chest X-ray and retinal images. We use the Tuberculosis chest X-ray (Shenzhen) dataset and the STARE dataset of retinal images for fine-tuning the model. The proposed model achieves superior perceptual quality compared to the Real-ESRGAN model, effectively preserving fine details and generating images with more realistic textures.
Distributional Drift Detection in Medical Imaging with Sketching and Fine-Tuned Transformer
Department of Neurology, University of Miami, FL USA（迈阿密大学神经病学系）；Department of Computer Science & IDSC, University of Miami, FL USA（迈阿密大学计算机科学与IDSC系）；Georgetown University Medical Center, Washington D.C. USA（乔治城大学医学中心）
Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?
Arizona State University（亚利桑那州立大学）；Mayo Clinic（梅奥诊所）
从头开始训练深度卷积神经网络（CNN）是很困难的，因为它需要大量的标记训练数据和大量的专业知识来确保适当的收敛。一个有希望的替代方案是微调CNN，该CNN已经使用例如大量标记的自然图像进行了预训练。然而，自然图像和医学图像之间的实质性差异可能会反对这种知识转移。在本文中，我们试图回答医学图像分析背景下的以下中心问题：\emph{Can the use of pre-trained deep CNNs with sufficient fine-tuning eliminate the need for training a deep CNN from scratch?}为了解决这个问题，我们考虑了3个专业的4种不同的医学成像应用（放射学、心脏病学和胃肠病学），涉及3种不同成像模式的分类、检测和分割，并研究了从头开始训练的深度CNN的性能如何与以逐层方式微调的预训练CNN进行比较。我们的实验一致表明，（1）使用具有足够微调的预训练的CNN优于或在最坏情况下表现得与从头开始训练的CNN一样好;（2）微调的CNN比从头开始训练的CNN对训练集的大小更鲁棒;（3）浅调或深调都不是特定应用的最佳选择;以及（4）我们的逐层微调方案可以提供一种实用的方法，以根据可用数据的量来达到当前应用的最佳性能。
DS@GT ARC at ImageCLEFmedical 2026: Architectural Diversity for Concept Detection and Foundation-Model Scaling for Caption Prediction in Medical Image Analysis
Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis
Eindhoven University of Technology（埃因霍温理工大学）；Erasmus Medical Center（伊拉斯谟医学中心）；University of Copenhagen（哥本哈根大学）；University Medical Center Utrecht（乌得勒支大学医学中心）
Surrogate Supervision for Medical Image Analysis: Effective Deep Learning From Limited Quantities of Labeled Data
VoxelCloud, Inc.（体素云公司）；Second Military Medical University（第二军医大学）；Changzheng Hospital（长征医院）；Shanghai Jiaotong University School of Medicine（上海交通大学医学院）；Ruijin Hospital（瑞金医院）
General Vision Encoder Features as Guidance in Medical Image Registration
Klinikum Rechts der Isar, Technical University of Munich（慕尼黑工业大学右岸医院）；School of Computation, Information & Technology, Technical University of Munich（慕尼黑工业大学计算、信息与技术学院）；Institute of Machine Learning in Biomedical Imaging, Helmholtz Munich（亥姆霍兹慕尼黑中心生物医学影像机器学习研究所）；Munich Center for Machine Learning（慕尼黑机器学习中心）；Cancer Research UK Cambridge Institute, University of Cambridge（剑桥大学英国癌症研究剑桥研究所）；Department of Oncology, University of Cambridge（剑桥大学肿瘤学系）；German Cancer Consortium (DKTK), Partner Site Munich（德国癌症联盟慕尼黑合作站点）；Department of Computing, Imperial College London（伦敦帝国理工学院计算机系）
Bringing CLIP to the Clinic: Dynamic Soft Labels and Negation-Aware Learning for Medical Analysis
Interdisciplinary Program in Bioengineering, Seoul National University Graduate School（生物工程跨学科项目，首尔国立大学研究生院）；Integrated Major in Innovative Medical Science, Seoul National University Graduate School（创新医学联合专业，首尔国立大学研究生院）；Department of Radiology, Seoul National University Hospital（放射科，首尔国立大学医院）
