LLaTiSA is a vision-language model trained on a new 83k-sample hierarchical time series reasoning dataset that shows superior performance and out-of-distribution generalization on stratified TSR tasks.
Teach multimodal llms to comprehend electro- cardiographic images.arXiv preprint arXiv:2410.19008
4 Pith papers cite this work. Polarity classification is still indexing.
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CardioThink applies structured clinical reasoning stages and Structured Set Policy Optimization (SSPO) to ECG classification, yielding higher diagnostic accuracy and more interpretable rationales than direct prediction baselines on multiple benchmarks.
DeepArrhythmia introduces a segment-contextualized multimodal framework for beat-level ECG arrhythmia classification that uses tool-grounded evidence extraction and selective acquisition routed by segment-level confidence.
PulseLM aggregates PPG data from 16 sources into 1M segments and 2.5M QA pairs for 12 tasks, providing a standardized benchmark for PPG-text multimodal learning.
citing papers explorer
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LLaTiSA: Towards Difficulty-Stratified Time Series Reasoning from Visual Perception to Semantics
LLaTiSA is a vision-language model trained on a new 83k-sample hierarchical time series reasoning dataset that shows superior performance and out-of-distribution generalization on stratified TSR tasks.
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Reasoning Before Diagnosis: Physician-Inspired Structured Thinking for ECG Classification
CardioThink applies structured clinical reasoning stages and Structured Set Policy Optimization (SSPO) to ECG classification, yielding higher diagnostic accuracy and more interpretable rationales than direct prediction baselines on multiple benchmarks.
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DeepArrhythmia: Segment-Contextualized ECG Arrhythmia Classification via Selective Evidence Acquisition
DeepArrhythmia introduces a segment-contextualized multimodal framework for beat-level ECG arrhythmia classification that uses tool-grounded evidence extraction and selective acquisition routed by segment-level confidence.
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PulseLM: A Foundation Dataset and Benchmark for PPG-Text Learning
PulseLM aggregates PPG data from 16 sources into 1M segments and 2.5M QA pairs for 12 tasks, providing a standardized benchmark for PPG-text multimodal learning.