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MosMedData: Chest CT Scans With COVID-19 Related Findings Dataset

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arxiv 2005.06465 v1 pith:PG42JDOX submitted 2020-05-13 cs.CY cs.LGeess.IV

S.P. Morozov , A.E. Andreychenko , N.A. Pavlov , A.V. Vladzymyrskyy , N.V. Ledikhova , V.A. Gombolevskiy , I.A. Blokhin , P.B. Gelezhe
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A.V. Gonchar V.Yu. Chernina
This is my paper · ORCID
classification cs.CYcs.LGeess.IV
keywords datasetcovid-19findingsscanschestrelatedannotatedanonymised
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This dataset contains anonymised human lung computed tomography (CT) scans with COVID-19 related findings, as well as without such findings. A small subset of studies has been annotated with binary pixel masks depicting regions of interests (ground-glass opacifications and consolidations). CT scans were obtained between 1st of March, 2020 and 25th of April, 2020, and provided by municipal hospitals in Moscow, Russia. Permanent link: https://mosmed.ai/datasets/covid19_1110. This dataset is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0) License. Key words: artificial intelligence, COVID-19, machine learning, dataset, CT, chest, imaging

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Cited by 10 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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    A localization-infused vision-language fusion method converts textual location cues into multi-scale localization predictions and uses them to guide medical image segmentation, outperforming prior methods on three benchmarks.

  2. Semi-MedRef: Semi-Supervised Medical Referring Image Segmentation with Cross-Modal Alignment

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    Semi-MedRef introduces T-PatchMix, PosAug, and ITCL within a teacher-student SSL setup to preserve image-text alignment under augmentation for medical referring segmentation on QaTa-COV19 and MosMedData+.

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  4. INSIGHT: Explainable Weakly-Supervised Medical Image Analysis

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    INSIGHT, a weakly-supervised aggregator with built-in heatmap generation, achieves strong classification and segmentation on CT and whole-slide pathology benchmarks using only image-level labels.

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    A validation-tuned, test-time adaptive ensemble of frozen biomedical vision experts improves classification, segmentation, and multimodal diagnosis across nine datasets without updating expert weights.

  7. Decoupling Language Guidance from Backbones for Text-Guided Medical Segmentation

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    A shape-preserving hierarchical adapter plus coarse-to-fine supervision lets the same text-guidance module work across convolutional and transformer vision backbones and multiple medical language encoders, improving f...

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    DD-CMD combines text-guided spatial cross-attention with DCT-based spectral FiLM modulation and reports higher Dice/mIoU than prior text-guided models on QaTa-COV19 and MosMedData+.

  9. Deep-Learning-Assisted Highly-Accurate COVID-19 Diagnosis on Lung Computed Tomography Images

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