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Found 15 papers, 6 papers with code
An Enhanced Harmonic Densely Connected Hybrid Transformer Network Architecture for Chronic Wound Segmentation Utilising Multi-Colour Space Tensor Merging
This paper presents the first study to focus on darker-skin tones for chronic wound segmentation using models trained only on wound images exhibiting lighter skin.
SALT: Introducing a Framework for Hierarchical Segmentations in Medical Imaging using Softmax for Arbitrary Label Trees
Performance was assessed using the Dice score across various datasets, including SAROS, CT-ORG, FLARE22, LCTSC, LUNA16, and WORD.
WisPerMed at BioLaySumm: Adapting Autoregressive Large Language Models for Lay Summarization of Scientific Articles
Out of 54 participants, the WisPerMed team reached the 4th place, measured by readability, factuality, and relevance.
WisPerMed at "Discharge Me!": Advancing Text Generation in Healthcare with Large Language Models, Dynamic Expert Selection, and Priming Techniques on MIMIC-IV
Various strategies were employed, including few-shot learning, instruction tuning, and Dynamic Expert Selection (DES), to develop models capable of generating the required text sections.
ROCOv2: Radiology Objects in COntext Version 2, an Updated Multimodal Image Dataset
The dataset is suitable for training image annotation models based on image-caption pairs, or for multi-label image classification using Unified Medical Language System (UMLS) concepts provided with each image.
Comprehensive Study on German Language Models for Clinical and Biomedical Text Understanding
Recent advances in natural language processing (NLP) can be largely attributed to the advent of pre-trained language models such as BERT and RoBERTa.
On the Impact of Cross-Domain Data on German Language Models
Traditionally, large language models have been either trained on general web crawls or domain-specific data.
PreprintResolver: Improving Citation Quality by Resolving Published Versions of ArXiv Preprints using Literature Databases
Experiments were performed on a sample of 1, 000 arXiv-preprints from the research field of computer science and without any publication information.
Why is the winner the best?
The "typical" lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning.
Biomedical image analysis competitions: The state of current participation practice
Of these, 84% were based on standard architectures.
Domain Adaptation of Transformer-Based Models using Unlabeled Data for Relevance and Polarity Classification of German Customer Feedback
For the subtask Relevance Classification, the best models achieve a micro-averaged $F1$-Score of 96. 1 % on the first test set and 95. 9 % on the second one, and a score of 85. 1 % and 85. 3 % for the subtask Polarity Classification.
Machine Learning Workflow to Explain Black-box Models for Early Alzheimer's Disease Classification Evaluated for Multiple Datasets
The most important RF features to predict AD conversion were the volume of the amygdalae, and a cognitive test score.
Boosting EfficientNets Ensemble Performance via Pseudo-Labels and Synthetic Images by pix2pixHD for Infection and Ischaemia Classification in Diabetic Foot Ulcers
Performances of models and ensembles trained on the baseline and extended training dataset are compared.
Deep Learning in Diabetic Foot Ulcers Detection: A Comprehensive Evaluation
DFUC2020 provided participants with a comprehensive dataset consisting of 2, 000 images for training and 2, 000 images for testing.
Utilizing Neural Networks and Linguistic Metadata for Early Detection of Depression Indications in Text Sequences
In particular, a convolutional neural network based on different word embeddings is evaluated and compared to a classification based on user-level linguistic metadata.
