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ENTITY MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports

MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports

PulseAugur coverage of MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports — every cluster mentioning MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports across labs, papers, and developer communities, ranked by signal.

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TIER MIX · 90D

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SENTIMENT · 30D

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RECENT · PAGE 1/1 · 6 TOTAL
  1. TOOL · CL_25772 ·

    Causal model enhances interpretability of chest X-ray diagnoses

    Researchers have developed XpertCausal, a novel causal concept bottleneck model designed to enhance the interpretability of chest X-ray interpretations. This model explicitly models the generative process of diseases pr…

  2. TOOL · CL_15799 ·

    CXRMate-2 model generates clinically acceptable chest X-ray reports

    Researchers have developed CXRMate-2, a novel model for generating radiology reports from chest X-rays. This model utilizes structured multimodal temporal embeddings and reinforcement learning to improve semantic alignm…

  3. RESEARCH · CL_15537 ·

    New AI model GDMRG improves medical report generation with topological knowledge

    Researchers have developed a new framework called GDMRG for automated medical report generation, aiming to improve diagnostic accuracy and efficiency. This system incorporates a Topological Knowledge Internalization mod…

  4. RESEARCH · CL_11390 ·

    RIHA Transformer aligns radiology images and reports hierarchically for better generation

    Researchers have developed RIHA, a novel framework for radiology report generation that addresses the challenge of aligning complex visual features with the hierarchical structure of medical reports. Unlike previous met…

  5. RESEARCH · CL_06578 ·

    LoFi method enhances fine-grained representation learning for chest X-rays

    Researchers have introduced LoFi, a novel method for learning fine-grained representations in chest X-rays. This approach addresses limitations in existing contrastive models by incorporating location-aware captioning t…

  6. RESEARCH · CL_06313 ·

    Quantum kernels show advantage over classical methods in medical AI embeddings

    A new paper presents evidence for quantum kernel advantage in medical foundation model embeddings, specifically for binary insurance classification tasks on MIMIC-CXR chest radiographs. Using quantum support vector mach…