Browse by research highlights:

Precision Cancer Care

Histology Image Analysis

Real-World Healthcare Application

Foundational AI Research

Bridging the Faithfulness Gap in Prototypical Models

BMIRDS . 5/21/25 BMIRDS . 5/21/25

Bridging the Faithfulness Gap in Prototypical Models

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Communication Makes Perfect: Persuasion Dataset Construction via Multi-LLM Communication

BMIRDS . 4/23/25 BMIRDS . 4/23/25

Communication Makes Perfect: Persuasion Dataset Construction via Multi-LLM Communication

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ImpScore: A Learnable Metric For Quantifying The Implicitness Level of Sentence

BMIRDS . 4/14/25 BMIRDS . 4/14/25

ImpScore: A Learnable Metric For Quantifying The Implicitness Level of Sentence

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A novel framework for the automated characterization of Gram-stained blood culture slides using a large-scale vision transformer

BMIRDS . 2/24/25 BMIRDS . 2/24/25

A novel framework for the automated characterization of Gram-stained blood culture slides using a large-scale vision transformer

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A Benchmark for Long-Form Medical Question Answering

BMIRDS . 12/9/24 BMIRDS . 12/9/24

A Benchmark for Long-Form Medical Question Answering

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IvRA: A Framework to Enhance Attention-Based Explanations for Language Models with Interpretability-Driven Training

BMIRDS . 9/21/24 BMIRDS . 9/21/24

IvRA: A Framework to Enhance Attention-Based Explanations for Language Models with Interpretability-Driven Training

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Masked pre-training of transformers for histology image analysis

BMIRDS . 5/31/24 BMIRDS . 5/31/24

Masked pre-training of transformers for histology image analysis

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Proto-lm: A Prototypical Network-Based Framework for Built-in Interpretability in Large Language Models

Naofumi Tomita 12/6/23 Naofumi Tomita 12/6/23

Proto-lm: A Prototypical Network-Based Framework for Built-in Interpretability in Large Language Models

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Improving Syntactic Probing Correctness and Robustness with Control Tasks

Naofumi Tomita 7/9/23 Naofumi Tomita 7/9/23

Improving Syntactic Probing Correctness and Robustness with Control Tasks

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Towards Interpretable Deep Reinforcement Learning Models via Inverse Reinforcement Learning

BMIRDS . 11/29/22 BMIRDS . 11/29/22

Towards Interpretable Deep Reinforcement Learning Models via Inverse Reinforcement Learning

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Naofumi Tomita 10/6/20 Naofumi Tomita 10/6/20

Multi-Ontology Refined Embeddings (MORE): A Hybrid Multi-Ontology and Corpus-based Semantic Representation Model for Biomedical Concepts

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