35 citations · 60 across the 11 of their papers we have counts for
11 papers
Modular Expert Merging for Biomedical Retrieval
Sameh Khattab, Jean-Philippe Corbeil, Osman Alperen Çinar-Koraş +5
Adapting general-purpose LLMs into domain-specialized dense retrievers typically requires large-scale training on mixed-domain data. We show that merging independently trained doma…
Automatic Fine-grained Segmentation-assisted Report Generation
Frederic Jonske, Constantin Seibold, Osman Alperen Koras +6
Reliable end-to-end clinical report generation has been a longstanding goal of medical ML research. The end goal for this process is to alleviate radiologists' workloads and provid…
A Modular Approach for Clinical SLMs Driven by Synthetic Data with Pre-Instruction Tuning, Model Merging, and Clinical-Tasks Alignment
Jean-Philippe Corbeil, Amin Dada, Jean-Michel Attendu +7
High computation costs and latency of large language models such as GPT-4 have limited their deployment in clinical settings. Small language models (SLMs) offer a cost-effective al…
Towards Conditioning Clinical Text Generation for User Control
Osman Alperen Koraş, Rabi Bahnan, Jens Kleesiek +1
Deploying natural language generation systems in clinical settings remains challenging despite advances in Large Language Models (LLMs), which continue to exhibit hallucinations an…
MeDiSumQA: Patient-Oriented Question-Answer Generation from Discharge Letters
Amin Dada, Osman Alperen Koras, Marie Bauer +4
While increasing patients' access to medical documents improves medical care, this benefit is limited by varying health literacy levels and complex medical terminology. Large langu…
Biomedical Large Languages Models Seem not to be Superior to Generalist Models on Unseen Medical Data
Felix J. Dorfner, Amin Dada, Felix Busch +8
Large language models (LLMs) have shown potential in biomedical applications, leading to efforts to fine-tune them on domain-specific data. However, the effectiveness of this appro…
Comprehensive Study on German Language Models for Clinical and Biomedical Text Understanding
Ahmad Idrissi-Yaghir, Amin Dada, Henning Schäfer +17
Recent advances in natural language processing (NLP) can be largely attributed to the advent of pre-trained language models such as BERT and RoBERTa. While these models demonstrate…
Does Biomedical Training Lead to Better Medical Performance?
Amin Dada, Marie Bauer, Amanda Butler Contreras +4
Large Language Models (LLMs) are expected to significantly contribute to patient care, diagnostics, and administrative processes. Emerging biomedical LLMs aim to address healthcare…
On the Impact of Cross-Domain Data on German Language Models
Amin Dada, Aokun Chen, Cheng Peng +12
Traditionally, large language models have been either trained on general web crawls or domain-specific data. However, recent successes of generative large language models, have she…
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
Jianning Li, Zongwei Zhou, Jiancheng Yang +154
Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from co…
FAM: Relative Flatness Aware Minimization
Linara Adilova, Amr Abourayya, Jianning Li +5
Flatness of the loss curve around a model at hand has been shown to empirically correlate with its generalization ability. Optimizing for flatness has been proposed as early as 199…