BioMed
BiomedNLP-BiomedBERT-base-uncased-abstractBiomedNLP-BiomedBERT-base-uncased-abstract-fulltextbiomedical-ner-allBiomedCLIP-PubMedBERT_256-vit_base_patch16_224OpenMed-NER-DiseaseDetect-BioMed-335MOpenMed-NER-OrganismDetect-BioMed-109MBiomedVLP-CXR-BERT-specializedOpenMed-NER-OrganismDetect-BioMed-335M
Datasets
All datasets matching “BioMed”CVPR-BiomedSegFMThis repository contains the BiomedSegFM dataset, a crucial resource for the CVPR 2025 Competition: Foundation Models for 3D Biomedical Image Segmentation.
Foundation Models for Interactive 3D Biomedical Image Segmentation (Homepage)
Foundation Models for Text-guided 3D Biomedical Image Segmentation (Homepage)
CVPR 2025 Competition: Foundation Models for 3D Biomedical Image Segmentation
Highly recommend watching the webinar recording to learn about the task settings and… See the full description on the dataset page: https://huggingface.co/datasets/junma/CVPR-BiomedSegFM.biomedica_webdataset_24M
Dataset Card for Dataset Name
Arxiv: Arxiv
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Website: Biomedica
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Training instructions: OpenCLIP
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Tutorial: Google Colab
BIOMEDICA Dataset is a large-scale, deep-learning-ready biomedical dataset containing over 24M imagecaption pairs and 30M image-references from 6M unique open-source articles. Each… See the full description on the dataset page: https://huggingface.co/datasets/BIOMEDICA/biomedica_webdataset_24M.SPACCC_Tokenizer
The Tokenizer for Clinical Cases Written in Spanish
Introduction
This repository contains the tokenization model trained using the SPACCC_TOKEN corpus (https://github.com/PlanTL-SANIDAD/SPACCC_TOKEN). The model was trained using the 90% of the corpus (900 clinical cases) and tested against the 10% (100 clinical cases). This model is a great resource to tokenize biomedical documents, specially clinical cases written in Spanish.
This model was created using the Apache… See the full description on the dataset page: https://huggingface.co/datasets/Biomedical-TeMU/SPACCC_Tokenizer.biomedical_lectures_v2
Vidore Benchmark 2 - MIT Dataset (Multilingual)
This dataset is part of the "Vidore Benchmark 2" collection, designed for evaluating visual retrieval applications. It focuses on the theme of MIT courses in anatomy (precisely tissue interactions).
Dataset Summary
The dataset contain queries in the following languages : ["english", "french", "german", "spanish"]. Each query was originaly in "english" (see… See the full description on the dataset page: https://huggingface.co/datasets/vidore/biomedical_lectures_v2.CodiEsp_corpus
Introduction
These are the train, development, test and background sets of the CodiEsp corpus. Train and development have gold standard annotations. The unannotated background and test sets are distributed together. All documents are released in the context of the CodiEsp track for CLEF ehealth 2020 (http://temu.bsc.es/codiesp/).
The CodiEsp corpus contains manually coded clinical cases. All documents are in Spanish language and CIE10 is the coding terminology (it is the Spanish… See the full description on the dataset page: https://huggingface.co/datasets/Biomedical-TeMU/CodiEsp_corpus.ProfNER_corpus_NER
Description
Gold standard annotations for profession detection in Spanish COVID-19 tweets
The entire corpus contains 10,000 annotated tweets. It has been split into training, validation, and test (60-20-20). The current version contains the training and development set of the shared task with Gold Standard annotations. In addition, it contains the unannotated test, and background sets will be released.
For Named Entity Recognition, profession detection, annotations are distributed… See the full description on the dataset page: https://huggingface.co/datasets/Biomedical-TeMU/ProfNER_corpus_NER.