Machine Learning for Biomedical Imaging

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🎯 Authors present a dataset for schistosomiasis diagnosis containing paired bright- and darkfield microscopy images from 728 patients in Côte d’Ivoire, supporting robust and field-ready diagnosis of S. haematobium infection. 🔎 Free to read: doi.org/10.59275/j.mel…

🚨 New publication alert: 📢 “A schistosomiasis dataset with bright- and darkfield images.” 🖊️ D K Silué, M Díaz de León Derby, C B Delahunt, A-L Le Ny, E Spencer, M Armstrong, K N Fisher, D A Fletcher, I I Bogoch, J T Coulibaly. ⬇️

🎯 Authors introduce a dataset of egocentric videos capturing emergency procedures in resource-constrained settings. With fine-grained annotations, it enables benchmarks for tasks like action recognition and visual question answering. 🔎 Free to read: doi.org/10.59275/j.mel…

🚨 New publication alert: 📢 “The Trauma THOMPSON Dataset for Real-World Emergency AI.” 🖊️ Y Zhuo, E Zhang, X Yu, A Pachpande, A W. Kirkpatrick, K Couperus, J Mckee, J Wachs. ⬇️

🎯 Authors introduce a dataset of 942 endoscopic images from 106 patients with oropharyngeal cancer. With expert pixel-level labels across 12 classes, it supports AI development for detection and segmentation in head and neck oncology. 🔎 Free to read: doi.org/10.59275/j.mel…

🚨 New publication alert: 📢 “SCOPE-HN: A Segmentation-based Collection of OroPharyngeal Structures Using Flexible Endoscopy for Head and Neck Cancers.” 🖊️ N Bedi, A Rau, A Paderno, H Vang, Y Kyoung So, F C Holsinger. ⬇️

🎯 Authors introduce an MRI dataset for multiple sclerosis with expert-reviewed lesion annotations, including patients and controls with demographic metadata, supporting robust development and evaluation of AI models for MS analysis. 🔎 Free to read: doi.org/10.59275/j.mel…

🚨 New publication alert: 📢 “SibBMS: Siberian Brain Multiple Sclerosis Dataset with lesion segmentation and patient meta information.” 🖊️ B N Tuchinov, A I Prokaeva, L M Vasilkiv, Y A Stankevich, D S Korobko, N A Malkova, A A Tulupov. ⬇️

🎯 Authors introduce the first histopathology dataset for mycetoma, including 864 annotated images from 142 patients with grain segmentation masks, supporting automated detection and classification in resource-limited settings. 🔎 Free to read: doi.org/10.59275/j.mel…

🚨 New publication alert: 📢 “MyData: A Comprehensive Database of Mycetoma Tissue Microscopic Images for Histopathological Analysis.” 🖊️ H O Ali, R Abraham, G Desoubeaux, A Fahal, C Tauber. ⬇️

🎯 Authors introduce an open-source pediatric hydrocephalus MRI dataset with expert segmentations of brain tissues, including the choroid plexus, a promising biomarker for hydrocephalus evaluation. 🔎 Free to read: doi.org/10.59275/j.mel…

🚨 New publication alert: 📢 “HyKid: An Open MRI Dataset with Expert-Annotated Multi-Structure and Choroid Plexus in Pediatric Hydrocephalus.” 🖊️ Y Xu, Y Ding, H Sun, H Zhang, L Zhao. ⬇️

🎯 Authors introduce SurgiSR4K, the first publicly available 4K robotic-assisted surgical video dataset. The dataset supports a wide range of high-resolution computer vision tasks in surgical imaging. 🔎 Free to read: doi.org/10.59275/j.mel…

🚨 New publication alert: 📢 “SurgiSR4K: A High‑Resolution Endoscopic Video Dataset for Robotic-Assisted Minimally Invasive Procedures.” 🖊️ F Jiang, X Zhang, L Jin, R Liang, Y Chen, A C Venkatesh, J Culman, T Wu, L Shao, W Sun, C Gao, H McNamara, J Lu, O Mohareri. ⬇️

🎯 Authors introduce the first public pediatric demyelinating lesion segmentation dataset, including MRI scans and rich clinical metadata beyond multiple sclerosis. 🔎 Free to read: doi.org/10.59275/j.mel…

🚨 New publication alert: 📢 “PediDemi - A Pediatric Demyelinating Lesion Segmentation Dataset.” 🖊️ M Popa, G A Vișa. ⬇️

🎯 Authors introduce BaMBo, a public dataset of 185 expert-annotated bone marrow biopsy images for automated cellularity assessment, supporting multi-class segmentation and advancing reproducible computational pathology tools. 🔎 Free to read: doi.org/10.59275/j.mel…

🚨 New publication alert: 📢 “BaMBo: An Annotated Bone Marrow Biopsy Dataset for Segmentation Task.” 🖊️ A Singh, S Dharamdasani, P Sharma, S Gupta. ⬇️

🎯 Authors present an enhanced version of the NIH malaria dataset with detailed COCO-format bounding box annotations for object detection. The revised dataset supports robust deep learning–based malaria detection. 🔎 Free to read: doi.org/10.59275/j.mel…

🚨 New publication alert: 📢 “A COCO-Formatted Instance-Level Dataset for Plasmodium Falciparum Detection in Giemsa-Stained Blood Smears.” 🖊️ F Wilm, L C Rivera Monroy, M Öttl, L Mürdter, L Mill, A Maier. ⬇️