Search Results for author: Thomas Lin

Found 9 papers, 2 papers with code

On Computational Limits of Modern Hopfield Models: A Fine-Grained Complexity Analysis

no code implementations7 Feb 2024 Jerry Yao-Chieh Hu, Thomas Lin, Zhao Song, Han Liu

Specifically, we establish an upper bound criterion for the norm of input query patterns and memory patterns.

Retrieval

3D-MIR: A Benchmark and Empirical Study on 3D Medical Image Retrieval in Radiology

1 code implementation23 Nov 2023 Asma Ben Abacha, Alberto Santamaria-Pang, Ho Hin Lee, Jameson Merkow, Qin Cai, Surya Teja Devarakonda, Abdullah Islam, Julia Gong, Matthew P. Lungren, Thomas Lin, Noel C Codella, Ivan Tarapov

The increasing use of medical imaging in healthcare settings presents a significant challenge due to the increasing workload for radiologists, yet it also offers opportunity for enhancing healthcare outcomes if effectively leveraged.

Medical Image Retrieval Retrieval

ACI-BENCH: a Novel Ambient Clinical Intelligence Dataset for Benchmarking Automatic Visit Note Generation

no code implementations3 Jun 2023 Wen-wai Yim, Yujuan Fu, Asma Ben Abacha, Neal Snider, Thomas Lin, Meliha Yetisgen

Here we present the Ambient Clinical Intelligence Benchmark (ACI-BENCH) corpus, the largest dataset to date tackling the problem of AI-assisted note generation from visit dialogue.

Benchmarking

An Investigation of Evaluation Metrics for Automated Medical Note Generation

1 code implementation27 May 2023 Asma Ben Abacha, Wen-wai Yim, George Michalopoulos, Thomas Lin

To study the correlation between the automatic metrics and manual judgments, we evaluate automatic notes/summaries by comparing the system and reference facts and computing the factual correctness, and the hallucination and omission rates for critical medical facts.

Hallucination Knowledge Graph Embedding +1

Explicit and Implicit Semantic Ranking Framework

no code implementations11 Apr 2023 Xiaofeng Zhu, Thomas Lin, Vishal Anand, Matthew Calderwood, Eric Clausen-Brown, Gord Lueck, Wen-wai Yim, Cheng Wu

The core challenge in numerous real-world applications is to match an inquiry to the best document from a mutable and finite set of candidates.

Learning-To-Rank Text Summarization

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