Hasil untuk "History of medicine. Medical expeditions"

Berta Echániz Martínez, Eduardo Bueno Vergara , Enrique Perdiguero-Gil

A través de un ejercicio crítico de historia local, se definen los elementos que determinaron cómo el cáncer se construyó social y culturalmente durante la primera mitad del siglo XX. El análisis de los agentes políticos, mediáticos y científicos y su interacción para dar forma al Pabellón del Cáncer del Hospital Provincial de Alicante, nos permite estudiar un proyecto de lucha contra un mal que, en estas décadas, se estaba configurando como un nuevo flagelo social. Para ello, se profundiza en el papel desempeñado por dos de los médicos que desarrollaron su carrera profesional vinculada a una emergente especialidad, la radiología, indispensable para conocer la configuración de la enfermedad en el periodo estudiado.

Hatice Çelik Yiğit, Ayten Altıntaş

Amaç: İsmâil Cürcânî (1042–1137), Selçuklu döneminde yaşamış, klasik İslâm tıbbının önde gelen hekimlerinden biridir. Bu çalışma, İsmâîl Cürcânî’nin Zahîre-i Hârizmşâhî adlı eserinin Türkçe tercümesi olan Kanûn el-İlâc ve Şifâ el-Emrâz li Küllî Mizâc adlı ansiklopedik tıp kitabında yer alan diş ağrıları ve tedavi metotlarını inceleyerek günümüz alfabesine kazandırmayı ve eserin Türk tıp tarihi içindeki yerini ve katkılarını ortaya koymayı amaçlamaktadır. Yöntem: Eserin Hüsrev Paşa Koleksiyonu (No. 463) ve Topkapı Sarayı Emanet Hazinesi Koleksiyonu (No. 1832)’nde yer alan yazma nüshaları incelenmiş; bu nüshalar, Farsça matbu edisyonla karşılaştırılmıştır. Transkripsiyon sürecinde, İSNAD Basit Transkripsiyon Kılavuzu esas alınmıştır. Bulgular: Diş ağrılarının sû-i mizâc kavramı çerçevesinde değerlendirilip maddeli ve maddesiz olmak üzere ikiye ayrıldığı tespit edilmiştir. Maddeli sû-i mizâc, vücutta biriken zararlı maddelerin dişe ulaşarak ağrıya sebep olduğu durumu ifade ederken, maddesiz sû-i mizâc, doğrudan mizacın dengesizliğiyle ilişkilendirilmiştir. Tedavi, hastalığa sebep olan maddenin vücuttan uzaklaştırılmasına dayanmakta olup, fasd (kan alma), ishal, gargara, tütsü, dağlama ve lokal ilaç uygulamaları gibi yöntemleri içermektedir. Dişin rengindeki değişim, aşınma, hareketlilik ve diş etindeki şişlik gibi belirtiler göz önünde bulundurularak teşhis yöntemleri sunulmuştur. Sonuç: Tercümesi Ebülfazl Mehmed Efendi (Ö.1574) tarafından yapılan eserin, orijinal yapısı büyük ölçüde muhafaza edilmiş, özellikle Türkçe konuşan geniş halk kitlesinin, bu değerli tıp bilgilerine erişimini sağlama amacı güdülmüştür. Diş ağrılarının tedavisinde dönemin tıbbî paradigmasına uygun şekilde mizaca dayalı bir yaklaşım benimsenmiş; hastalığın mahiyetine uygun ilaçlar tercih edilmiş ve yalnızca lokal belirtiler değil, sistemik etkenler de dikkate alınarak bütüncül bir tedavi anlayışı ortaya konmuştur.

S M A Sharif, Rizwan Ali Naqvi, Woong-Kee Loh

Medical image denoising is considered among the most challenging vision tasks. Despite the real-world implications, existing denoising methods have notable drawbacks as they often generate visual artifacts when applied to heterogeneous medical images. This study addresses the limitation of the contemporary denoising methods with an artificial intelligence (AI)-driven two-stage learning strategy. The proposed method learns to estimate the residual noise from the noisy images. Later, it incorporates a novel noise attention mechanism to correlate estimated residual noise with noisy inputs to perform denoising in a course-to-refine manner. This study also proposes to leverage a multi-modal learning strategy to generalize the denoising among medical image modalities and multiple noise patterns for widespread applications. The practicability of the proposed method has been evaluated with dense experiments. The experimental results demonstrated that the proposed method achieved state-of-the-art performance by significantly outperforming the existing medical image denoising methods in quantitative and qualitative comparisons. Overall, it illustrates a performance gain of 7.64 in Peak Signal-to-Noise Ratio (PSNR), 0.1021 in Structural Similarity Index (SSIM), 0.80 in DeltaE ($ΔE$), 0.1855 in Visual Information Fidelity Pixel-wise (VIFP), and 18.54 in Mean Squared Error (MSE) metrics.

Felix Buendía, Joaquín Gayoso-Cabada, José-Luis Sierra

In this paper, we describe an approach to transforming the huge amount of medical knowledge available in existing online medical collections into standardized learning packages ready to be integrated into the most popular e-learning platforms. The core of our approach is a tool called Clavy, which makes it possible to retrieve pieces of content in medical collections, to transform this content into meaningful learning units, and to export it in the form of standardized learning packages. In addition to describing the approach, we demonstrate its feasibility by applying it to the generation of IMS content packages from MedPix, a popular online database of medical cases in the domain of radiology.

Nikita Malik, Pratinav Seth, Neeraj Kumar Singh et al.

Deep learning has driven significant advances in medical image analysis, yet its adoption in clinical practice remains constrained by the large size and lack of transparency in modern models. Advances in interpretability techniques such as DL-Backtrace, Layer-wise Relevance Propagation, and Integrated Gradients make it possible to assess the contribution of individual components within neural networks trained on medical imaging tasks. In this work, we introduce an interpretability-guided pruning framework that reduces model complexity while preserving both predictive performance and transparency. By selectively retaining only the most relevant parts of each layer, our method enables targeted compression that maintains clinically meaningful representations. Experiments across multiple medical image classification benchmarks demonstrate that this approach achieves high compression rates with minimal loss in accuracy, paving the way for lightweight, interpretable models suited for real-world deployment in healthcare settings.

Wentao Chen, Tianming Xu, Weimin Zhou

Image denoising algorithms have been extensively investigated for medical imaging. To perform image denoising, penalized least-squares (PLS) problems can be designed and solved, in which the penalty term encodes prior knowledge of the object being imaged. Sparsity-promoting penalties, such as total variation (TV), have been a popular choice for regularizing image denoising problems. However, such hand-crafted penalties may not be able to preserve task-relevant information in measured image data and can lead to oversmoothed image appearances and patchy artifacts that degrade signal detectability. Supervised learning methods that employ convolutional neural networks (CNNs) have emerged as a popular approach to denoising medical images. However, studies have shown that CNNs trained with loss functions based on traditional image quality measures can lead to a loss of task-relevant information in images. Some previous works have investigated task-based loss functions that employ model observers for training the CNN denoising models. However, such training processes typically require a large number of noisy and ground-truth (noise-free or low-noise) image data pairs. In this work, we propose a task-based regularization strategy for use with PLS in medical image denoising. The proposed task-based regularization is associated with the likelihood of linear test statistics of noisy images for Gaussian noise models. The proposed method does not require ground-truth image data and solves an individual optimization problem for denoising each image. Computer-simulation studies are conducted that consider a multivariate-normally distributed (MVN) lumpy background and a binary texture background. It is demonstrated that the proposed regularization strategy can effectively improve signal detectability in denoised images.

Yanyan Wang, Kechen Song, Yuyuan Liu et al.

Semi-supervised 3D medical image segmentation aims to achieve accurate segmentation using few labelled data and numerous unlabelled data. The main challenge in the design of semi-supervised learning methods consists in the effective use of the unlabelled data for training. A promising solution consists of ensuring consistent predictions across different views of the data, where the efficacy of this strategy depends on the accuracy of the pseudo-labels generated by the model for this consistency learning strategy. In this paper, we introduce a new methodology to produce high-quality pseudo-labels for a consistency learning strategy to address semi-supervised 3D medical image segmentation. The methodology has three important contributions. The first contribution is the Cooperative Rectification Learning Network (CRLN) that learns multiple prototypes per class to be used as external knowledge priors to adaptively rectify pseudo-labels at the voxel level. The second contribution consists of the Dynamic Interaction Module (DIM) to facilitate pairwise and cross-class interactions between prototypes and multi-resolution image features, enabling the production of accurate voxel-level clues for pseudo-label rectification. The third contribution is the Cooperative Positive Supervision (CPS), which optimises uncertain representations to align with unassertive representations of their class distributions, improving the model's accuracy in classifying uncertain regions. Extensive experiments on three public 3D medical segmentation datasets demonstrate the effectiveness and superiority of our semi-supervised learning method.

Robert A. Rigby, Mikis D. Stasinopoulos, Achim Zeileis et al.

We read with interest the above article by Zavorsky (2025, Respiratory Medicine, doi:10.1016/j.rmed.2024.107836) concerning reference equations for pulmonary function testing. The author compares a Generalized Additive Model for Location, Scale, and Shape (GAMLSS), which is the standard adopted by the Global Lung Function Initiative (GLI), with a segmented linear regression (SLR) model, for pulmonary function variables. The author presents an interesting comparison; however there are some fundamental issues with the approach. We welcome this opportunity for discussion of the issues that it raises. The author's contention is that (1) SLR provides "prediction accuracies on par with GAMLSS"; and (2) the GAMLSS model equations are "complicated and require supplementary spline tables", whereas the SLR is "more straightforward, parsimonious, and accessible to a broader audience". We respectfully disagree with both of these points.

Sabba Saltanat, Wasim Ahmad, Abdul Ansari et al.

The process of embryogenesis has long fascinated scholars, both in ancient Unani medicine and modern science, as it holds the key to the formation and development of life. In Unani medicine, the understanding of embryogenesis is deeply rooted in the philosophy of life, emphasizing the importance of reproduction for species survival. This article delves into the Unani perspective on embryogenesis, highlighting the role of Manī (semen) and the interplay of Arkān (elements) in shaping the development of the embryo. Unani scholars have expounded on various aspects of embryogenesis, including the formation of essential organs, sex differentiation, and the roles of Quwwat tanasuliyya (reproductive faculty) in Manī production and fertilization. They also described a holistic view of embryonic development, from the formation of the Zubda (zygote) to the differentiation of vital organs, aligning with some principles in modern embryology.This article explores striking similarities between Unani and modern scientific concepts of embryogenesis, such as gastrulation, umbilical vessels, and sex differentiation. Additionally, it discusses aspects like quickening, lactational amenorrhea, and foetal presentation, where Unani insights align with contemporary medical knowledge.All the relevant literature on Unani medicine has been evaluated, assessed and analysed based on classical texts. Additionally, several papers in this regard were also searched using search engines, namely PubMed, Google Scholar, and ScienceDirect.The evolution of embryology as a scientific discipline has seen significant transformations, progressing from its early rudimentary stages to a more evidence-driven approach. This shift towards empirical science becomes readily apparent when examining the historical trajectory of embryological development.

Alba Lérida Jiménez

Se-Kwon JEONG

This paper traces how medical technologies to correct vision were introduced and changed in Korean society until the introduction of Orthokeratology called ‘Dream Lens’ in the late 1990s. First of all, I outlines the historical background of the introduction and spread of the relatively unfamiliar and expensive Orthokeratology, which is said to “cure” myopia and astigmatism by pressing the cornea. ‘Dream Lens’, a ‘lens for correcting corneal refractive error’, was a popular vision correction technology in terms of its name, treatment method, and effect. Not only was it introduced with a name similar to contact lens used instead of glasses from decades ago, but the way it was attached to and removed from the cornea was also similar. On the other hand, the public was already familiar with the principle of correcting the refractive index by pressing the cornea and improving visual acuity in the long term, just like LASIK which became popular in the mid-1990s. The use of contact lens which was similar in terms of the name ‘lens’ and the effect of ‘correcting vision’, and the trend of LASIK which was similar in principle of controlling corneal refraction, was a historical stage that helped soft landing of orthokeratology. However, from contact lens, vision correction technology did not settle down without any conflict. There was a conflict between medical experts traditionally responsible for optometry and production of spectacles and lens, and opticians who were newly in charge of that area. Ophthalmologists who have been in charge of optometry and prescriptions for a long time had no choice but to hand over some of the inspection areas to opticians due to the rapidly increasing number of opticians and the implementation of the optician system in 1989. And they had no choice but to watch the expansion of the business of opticians who manufactured glasses based on their own vision tests and sold them together with contact lens. Instead, corneal resection, which is not a technique for correcting visual acuity due to corneal refractive error, but a surgical technique for treating the corneal refractive error itself, has become an ophthalmologist's unique task. In addition, Dream Lens, which corrects corneal refractive error using a similar principle, has also become an object of professional medical practice because it required more precise examination and treatment than eyeglasses or contact lenses. By understanding the process by which vision correction technologies, from contact lens to dream lens, have been introduced into Korean society over the past few decades, this paper gives a new understanding how different medical technologies with the same or similar purposes are settling down, and the tension between experts in charge of them.

Che Liu, Anand Shah, Wenjia Bai et al.

Medical Vision-Language Pre-training (VLP) learns representations jointly from medical images and paired radiology reports. It typically requires large-scale paired image-text datasets to achieve effective pre-training for both the image encoder and text encoder. The advent of text-guided generative models raises a compelling question: Can VLP be implemented solely with synthetic images generated from genuine radiology reports, thereby mitigating the need for extensively pairing and curating image-text datasets? In this work, we scrutinize this very question by examining the feasibility and effectiveness of employing synthetic images for medical VLP. We replace real medical images with their synthetic equivalents, generated from authentic medical reports. Utilizing three state-of-the-art VLP algorithms, we exclusively train on these synthetic samples. Our empirical evaluation across three subsequent tasks, namely image classification, semantic segmentation and object detection, reveals that the performance achieved through synthetic data is on par with or even exceeds that obtained with real images. As a pioneering contribution to this domain, we introduce a large-scale synthetic medical image dataset, paired with anonymized real radiology reports. This alleviates the need of sharing medical images, which are not easy to curate and share in practice. The code and the dataset can be found in \href{https://github.com/cheliu-computation/MedSyn-RepLearn/tree/main}{https://github.com/cheliu-computation/MedSyn-RepLearn/tree/main}.

Yuhao Huang, Xin Yang, Lian Liu et al.

The Segment Anything Model (SAM) is the first foundation model for general image segmentation. It has achieved impressive results on various natural image segmentation tasks. However, medical image segmentation (MIS) is more challenging because of the complex modalities, fine anatomical structures, uncertain and complex object boundaries, and wide-range object scales. To fully validate SAM's performance on medical data, we collected and sorted 53 open-source datasets and built a large medical segmentation dataset with 18 modalities, 84 objects, 125 object-modality paired targets, 1050K 2D images, and 6033K masks. We comprehensively analyzed different models and strategies on the so-called COSMOS 1050K dataset. Our findings mainly include the following: 1) SAM showed remarkable performance in some specific objects but was unstable, imperfect, or even totally failed in other situations. 2) SAM with the large ViT-H showed better overall performance than that with the small ViT-B. 3) SAM performed better with manual hints, especially box, than the Everything mode. 4) SAM could help human annotation with high labeling quality and less time. 5) SAM was sensitive to the randomness in the center point and tight box prompts, and may suffer from a serious performance drop. 6) SAM performed better than interactive methods with one or a few points, but will be outpaced as the number of points increases. 7) SAM's performance correlated to different factors, including boundary complexity, intensity differences, etc. 8) Finetuning the SAM on specific medical tasks could improve its average DICE performance by 4.39% and 6.68% for ViT-B and ViT-H, respectively. We hope that this comprehensive report can help researchers explore the potential of SAM applications in MIS, and guide how to appropriately use and develop SAM.

Gina Del Piero

El Manifiesto filosófico contra los cometas (1681) y la Libra astronómica filosófica (1690) de Carlos de Sigüenza y Góngora (México, 1645-1700) han sido considerados por la crítica en su continuidad, como dos textos -uno más breve y otro más extenso- que representan una misma idea acerca de la naturaleza de los cometas: ellos no son ni causa ni señal de catástrofes. Pero en su tiempo, cada obra contó con una causa, un objetivo, una visibilidad y un público propios. Volver a poner en el centro de atención estas diferencias vinculadas a la materialidad de cada obra permitirá conocer mejor las condiciones de la circulación del discurso científico en el siglo XVII en el virreinato de Nueva España. Al exponer su conocimiento acerca de un fenómeno astronómico, como lo fue el cometa de 1680/1, Sigüenza advierte la necesidad de contar con un plan diversificado de comunicación de la ciencia: por un lado, interviene en la arena política estableciendo que el cometa no auguraba ni provocaría ninguna desgracia al nuevo gobierno; por otro, escribe un extenso y especializado tratado dirigido a matemáticos europeos para socializar sus mediciones y demostrar que es factible ser americano y docto a la vez.

Jaime Larry Benchimol

Resumo O texto aqui comentado reconstitui o cotidiano no Instituto Oswaldo Cruz no começo do século XX com base em depoimentos de antigos funcionários. “Os escravos são as mãos e os pés do senhor do engenho” – escreveu Antonil em 1711. Cada laboratório do instituto era como um pequeno engenho onde as mãos e os pés dos pesquisadores eram seus serventes, que executavam desde as tarefas mais desqualificadas até operações bem delicadas da pesquisa científica, atualmente confiadas a técnicos formados em escolas próprias. As habilidades dos primitivos técnicos, muitos recrutados ainda meninos nas oficinas da instituição, eram adquiridas empiricamente. O instituto era moderno por suas atividades, mas as relações de trabalho traziam as marcas de uma sociedade agrária e patriarcal, recém-egressa da escravidão.

Kumud Lakara, Matias Valdenegro-Toro

Trusting the predictions of deep learning models in safety critical settings such as the medical domain is still not a viable option. Distentangled uncertainty quantification in the field of medical imaging has received little attention. In this paper, we study disentangled uncertainties in image to image translation tasks in the medical domain. We compare multiple uncertainty quantification methods, namely Ensembles, Flipout, Dropout, and DropConnect, while using CycleGAN to convert T1-weighted brain MRI scans to T2-weighted brain MRI scans. We further evaluate uncertainty behavior in the presence of out of distribution data (Brain CT and RGB Face Images), showing that epistemic uncertainty can be used to detect out of distribution inputs, which should increase reliability of model outputs.

Chenyu You, Weicheng Dai, Yifei Min et al.

Contrastive learning has shown great promise over annotation scarcity problems in the context of medical image segmentation. Existing approaches typically assume a balanced class distribution for both labeled and unlabeled medical images. However, medical image data in reality is commonly imbalanced (i.e., multi-class label imbalance), which naturally yields blurry contours and usually incorrectly labels rare objects. Moreover, it remains unclear whether all negative samples are equally negative. In this work, we present ACTION, an Anatomical-aware ConTrastive dIstillatiON framework, for semi-supervised medical image segmentation. Specifically, we first develop an iterative contrastive distillation algorithm by softly labeling the negatives rather than binary supervision between positive and negative pairs. We also capture more semantically similar features from the randomly chosen negative set compared to the positives to enforce the diversity of the sampled data. Second, we raise a more important question: Can we really handle imbalanced samples to yield better performance? Hence, the key innovation in ACTION is to learn global semantic relationship across the entire dataset and local anatomical features among the neighbouring pixels with minimal additional memory footprint. During the training, we introduce anatomical contrast by actively sampling a sparse set of hard negative pixels, which can generate smoother segmentation boundaries and more accurate predictions. Extensive experiments across two benchmark datasets and different unlabeled settings show that ACTION significantly outperforms the current state-of-the-art semi-supervised methods.

Bas H. M. van der Velden, Hugo J. Kuijf, Kenneth G. A. Gilhuijs et al.

With an increase in deep learning-based methods, the call for explainability of such methods grows, especially in high-stakes decision making areas such as medical image analysis. This survey presents an overview of eXplainable Artificial Intelligence (XAI) used in deep learning-based medical image analysis. A framework of XAI criteria is introduced to classify deep learning-based medical image analysis methods. Papers on XAI techniques in medical image analysis are then surveyed and categorized according to the framework and according to anatomical location. The paper concludes with an outlook of future opportunities for XAI in medical image analysis.

Florin C. Ghesu, Bogdan Georgescu, Awais Mansoor et al.

The interpretation of medical images is a challenging task, often complicated by the presence of artifacts, occlusions, limited contrast and more. Most notable is the case of chest radiography, where there is a high inter-rater variability in the detection and classification of abnormalities. This is largely due to inconclusive evidence in the data or subjective definitions of disease appearance. An additional example is the classification of anatomical views based on 2D Ultrasound images. Often, the anatomical context captured in a frame is not sufficient to recognize the underlying anatomy. Current machine learning solutions for these problems are typically limited to providing probabilistic predictions, relying on the capacity of underlying models to adapt to limited information and the high degree of label noise. In practice, however, this leads to overconfident systems with poor generalization on unseen data. To account for this, we propose a system that learns not only the probabilistic estimate for classification, but also an explicit uncertainty measure which captures the confidence of the system in the predicted output. We argue that this approach is essential to account for the inherent ambiguity characteristic of medical images from different radiologic exams including computed radiography, ultrasonography and magnetic resonance imaging. In our experiments we demonstrate that sample rejection based on the predicted uncertainty can significantly improve the ROC-AUC for various tasks, e.g., by 8% to 0.91 with an expected rejection rate of under 25% for the classification of different abnormalities in chest radiographs. In addition, we show that using uncertainty-driven bootstrapping to filter the training data, one can achieve a significant increase in robustness and accuracy.