Visual explainability of 250 skin diseases viewed through the eyes of an AI‐based, self‐supervised vision transformer—A clinical perspective by Ramy Abdel Mawgoud, Christian Posch

“…Recently, self‐supervised (SS) Vision Transformers have emerged, capturing complex visual patterns in hundreds of classes without any need for tedious image annotation. Objectives This study aimed to form the basis for an inexpensive and explainable AI system, targeted at the vastness of clinical skin diagnoses by comparing so‐called ‘self‐attention maps’ of an SS and a supervised ViT on 250 skin diseases—visualizations showing areas of interest for each skin disease. …”
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High performance with fewer labels using semi-weakly supervised learning for pulmonary embolism diagnosis by Zixuan Hu, Hui Ming Lin, Shobhit Mathur, Robert Moreland, Christopher D. Witiw, Laura Jimenez-Juan, Matias F. Callejas, Djeven P. Deva, Ervin Sejdić, Errol Colak

“…Abstract This study proposes a semi-weakly supervised learning approach for pulmonary embolism (PE) detection on CT pulmonary angiography (CTPA) to alleviate the resource-intensive burden of exhaustive medical image annotation. Attention-based CNN-RNN models were trained on the RSNA pulmonary embolism CT dataset and externally validated on a pooled dataset (Aida and FUMPE). …”
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A Deep Learning Approach for Image Analysis and Reading Body Weight From Digital Scales in Pigs Farms by Nicolas A. Reyes-Reyes, Mihai Catalin Doja, Pol Llagostera-Blasco, Lluis M. Pla-Aragones, Marcela C. Gonzalez-Araya

“…Therefore, our approach is reliable to support decisions in pig fattening management and suitable to be embedded into real-time weighing systems and useful too for image annotation purposes.…”
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An open-source platform for structured annotation and computational workflows in digital pathology research by Luca Lianas, Mauro Del Rio, Luca Pireddu, Oskar Aspegren, Francesca Giunchi, Michelangelo Fiorentino, Simone Leo, Renata Zelic, Per Henrik Vincent, Nicolas Destefanis, Daniela Zugna, Lorenzo Richiardi, Andreas Pettersson, Olof Akre, Francesca Frexia

“…Its main features include: (1) structured, multi-label morphological and clinical image annotation; (2) support for controlled but customisable annotation protocols; (3) dedicated annotation tools to facilitate enhanced accuracy, efficiency and consistency in the annotation process; and (4) workflow-based computational analysis with integrated provenance tracking. …”
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Research on machine learning methods for detecting objects in difficult shooting conditions by Vitalii Serdechnyi, Olesia Barkovska, Andriy Kovalenko, Anton Havrashenko, Vitalii Martovytskyi

“…The methods used are: convolutional neural networks, automated image annotation, comparative analysis of quality metrics (F1-score, mAP@0.5:.95, Precision, Recall, IoU, FPS), and manual correction of annotations. …”
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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

“…Tackling this segmentation task with deep learning (DL) methods is laborious due to the big burden of manual image annotation, expensive due to the high acquisition costs of 3D micro-CT images, and difficult due to embryonic cartilage’s complex and rapidly changing shapes. …”
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Usability of the BigO system in pediatric obesity treatment: A mixed-methods evaluation of clinical end-users by Niamh Arthurs, Sarah Browne, Rebekah Boardman, Shane O'Donnell, Gerardine Doyle, Tahar Kechadi, Arsalan Shahid, Louise Tully, Grace O’Malley

“…Technical barriers reported by adolescents included notifications of battery optimization, misunderstanding image annotation language, and compatibility challenges with certain phone models. …”
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Best practices for AI-based image analysis applications in aquatic sciences: The iMagine case study by Elnaz Azmi, Khadijeh Alibabaei, Valentin Kozlov, Tjerk Krijger, Gabriele Accarino, Sakina-Dorothée Ayata, Amanda Calatrava, Marco Mariano De Carlo, Wout Decrop, Donatello Elia, Sandro Luigi Fiore, Marco Francescangeli, Jean-Olivier Irisson, Rune Lagaisse, Martin Laviale, Antoine Lebeaud, Carolin Leluschko, Enoc Martínez, Germán Moltó, Igor Ruiz Atake, Antonio Augusto Sepp Neves, Damian Smyth, Jesús Soriano-González, Muhammad Arabi Tayyab, Vanessa Tosello, Álvaro López García, Dick Schaap, Gergely Sipos

“…The platform offers trained models as a service, integrating AI tools for image annotation, ensuring the creation of high-quality datasets that comply with FAIR principles. …”
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Highly precise community science annotations of video camera‐trapped fauna in challenging environments by Mimi Arandjelovic, Colleen R. Stephens, Paula Dieguez, Nuria Maldonado, Gaëlle Bocksberger, Marie‐Lyne Després‐Einspenner, Benjamin Debetencourt, Vittoria Estienne, Ammie K. Kalan, Maureen S. McCarthy, Anne‐Céline Granjon, Veronika Städele, Briana Harder, Lucia Hacker, Anja Landsmann, Laura K. Lynn, Heidi Pfund, Zuzana Ročkaiová, Kristeena Sigler, Jane Widness, Heike Wilken, Antonio Buzharevski, Adeelia S. Goffe, Kristin Havercamp, Lydia L. Luncz, Giulia Sirianni, Erin G. Wessling, Roman M. Wittig, Christophe Boesch, Hjalmar S. Kühl

“…To overcome limitations with image annotation, camera trap studies are increasingly linked to community science (CS) platforms. …”
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Biomedical Data Annotation: An OCT Imaging Case Study by Matthew Anderson, Salman Sadiq, Muzammil Nahaboo Solim, Hannah Barker, David H. Steel, Maged Habib, Boguslaw Obara

“…In this study, we evaluate the quality of diabetic macular edema (DME) intraretinal fluid (IRF) biomarker image annotations on OCT B-scans from five clinicians with a range of experience. …”
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Dissecting In Vivo Cross-Linker Performance: Insights into Structural Dynamics and Molecular Rigidity by Yuxuan Zhou, Beirong Zhang, Bowen Zhong, Min Sun, Xu Zhang, Yukui Zhang, Lihua Zhang, Qun Zhao, Zhou Gong, Maili Liu

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ShrimpDiseaseBD: An image dataset for detecting shrimp diseases in the aquaculture sector of BangladeshMendeley Data by Mohammad Manzurul Islam, Anabil Sarker, Ashiquzzaman Choudhury, Noortaz Ahmed, Ahmed Abdal Shafi, Nishat Tasnim Niloy, Md Shorif Hossain, Md Sawkat Ali, Abdullahi Chowdhury, Md. Hasanul Ferdaus

“…The dataset includes 1149 original images, with diseased shrimp images annotated to improve detection capabilities. This dataset is expected to be valuable for detecting shrimp diseases with precision and timing and is likely to encourage research and practical applications in automated shrimp health monitoring. …”
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RETINA: Reconstruction-based pre-trained enhanced TransUNet for electron microscopy segmentation on the CEM500K dataset. by Cheng Xing, Ronald Xie, Gary D Bader

“…We developed the RETINA method, which combines pre-training on the large, unlabeled CEM500K EM image dataset with a hybrid neural-network model architecture that integrates both local (convolutional layer) and global (transformer layer) image processing to learn from manual image annotations. RETINA outperformed existing models on cellular structure segmentation on five public EM datasets. …”
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The Method of Self-supervised Cervical Cell Classification by GAI Jin-ping, QIN Jian, HE Yong-jun, PENG Chen-hui

“…This results in an insufficient number of cervical cell image annotations, thus limiting further cervical cell classification performance improvements. …”
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Temperature-related intensity change (TRIC)-based high-throughput screening enables the discovery of small molecule CD28 binders by Laura Calvo-Barreiro, Saurabh Upadhyay, Moustafa T. Gabr

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Phosphorus metabolism in the brain of cognitively normal midlife individuals at risk for Alzheimer's disease by Prodromos Parasoglou, Ricardo S. Osorio, Oleksandr Khegai, Zanetta Kovbasyuk, Margo Miller, Amanda Ho, Seena Dehkharghani, Thomas Wisniewski, Antonio Convit, Lisa Mosconi, Ryan Brown

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Machine Learning Models for the Classification of Histopathological Images of Colorectal Cancer by Nektarios Georgiou, Pavlos Kolias, Ioanna Chouvarda

“…A comprehensive dataset of colon cancer images annotated into eight distinct categories based on their representation of cancerous cell portions was used. …”
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LADOS: Aerial Imagery Dataset for Oil Spill Detection, Classification, and Localization Using Semantic Segmentation by Konstantinos Gkountakos, Maria Melitou, Konstantinos Ioannidis, Konstantinos Demestichas, Stefanos Vrochidis, Ioannis Kompatsiaris

“…LADOS comprises 3388 images annotated at the pixel level across six distinct classes, including the background. …”
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Assisting human annotation of marine images with foundation models by Eric C. Orenstein, Eric C. Orenstein, Benjamin Woodward, Lonny Lundsten, Kevin Barnard, Brian Schlining, Kakani Katjia

“…Here we consider the utility of this approach for ocean imagery, leveraging Meta’s Segment Anything Model to enrich ocean image annotations based on existing labels. This workflow yields promising results, especially for modernizing existing data repositories. …”
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BMT: A Cross-Validated ThinPrep Pap Cervical Cytology Dataset for Machine Learning Model Training and Validation by E. Celeste Welch, Chenhao Lu, C. James Sung, Cunxian Zhang, Anubhav Tripathi, Joyce Ou

“…However, most publicly available datasets consist of pre-segmented single cell images, contain on-image annotations that must be manually edited out, or are prepared using the conventional Pap smear method. …”
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