Pith. sign in

REVIEW 9 cited by

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

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

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

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

classification cs.CY cs.LGeess.IV
keywords datasetcovid-19findingsscanschestrelatedannotatedanonymised
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original abstract

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

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 9 Pith papers

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

  1. Localization-Infused Vision-Language Semantic Fusion for Text-Guided Medical Image Segmentation

    cs.CV 2026-07 conditional novelty 6.0

    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. Beyond Visual Cues: CoT-Enhanced Reasoning for Semi-supervised Medical Image Segmentation

    cs.CV 2026-06 unverdicted novelty 6.0

    CERS integrates LLM-generated CoT reasoning, a knowledge pool, semantic reference selection, and a multi-scale attention module to improve semi-supervised medical image segmentation beyond visual pattern matching.

  3. Segmentation, Detection and Explanation: A Unified Framework for CT Appearance Reasoning

    cs.CV 2026-05 unverdicted novelty 6.0

    A unified autoregressive vision-language framework integrates segmentation, detection, and appearance reasoning for CT images via task-routing tokens and progressive refinement, with gains on public benchmarks.

  4. OPERA: Offline Policy-guided Expert Routing and Adaptation for Universal Biomedical Image Analysis

    cs.CV 2026-07 conditional novelty 5.0

    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.

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

    cs.CV 2026-07 conditional novelty 5.0

    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...

  6. APRIL-MedSeg: A Modular Medical Image Segmentation Toolbox Embracing Modern Paradigms

    cs.CV 2026-06 unverdicted novelty 5.0

    APRIL-MedSeg is a new open-source modular toolbox that uses YAML configuration and component registries to unify multiple advanced paradigms for medical image segmentation.

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

    cs.CV 2026-05 unverdicted novelty 5.0

    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+.

  8. Deep Reprogramming Distillation for Medical Foundation Models

    cs.CV 2026-05 unverdicted novelty 5.0

    DRD introduces a reprogramming module and CKA-based distillation to enable efficient, robust adaptation of medical foundation models to downstream 2D/3D classification and segmentation tasks, outperforming prior PEFT ...

  9. APRIL-MedSeg: A Modular Medical Image Segmentation Toolbox Embracing Modern Paradigms

    cs.CV 2026-06 unverdicted novelty 4.0

    Presents APRIL-MedSeg, a modular YAML-configurable toolbox for 2D medical image segmentation integrating semi-supervised, domain adaptation, distillation, weakly supervised, text-guided, and foundation model paradigms...