Update README.md

README.md

@@ -90,10 +90,25 @@ The data will follow the structure below:
 - Caption Filtering: The dataset is further refined by filtering captions that contain keywords like 'ct', 'lung', or 'chest'.
 - Image Labeling: Finally, the images are chosen based on the presence of labels 'ct' and 'lung'. This dual-label requirement ensures that the selected images are relevant to CT scans of the lungs, which are instrumental in detecting and assessing tuberculosis.
 - Through this meticulous filtering process, an initial tuberculosis dataset is compiled from the broader MultiCaRe Dataset. This dataset is messy, contains many diffferent files.
-2. To enhance the quality and relevance of the tuberculosis dataset, additional processing steps are implemented after the initial filtration from the MultiCaRe Dataset:
+2. To enhance the quality and relevance of the tuberculosis dataset, additional processing steps are implemented after in the Hugging Face python script after the initial filtration from the MultiCaRe Dataset:
 - Exclusion of Records with Missing Age Information.
-- Data type conversion of medical images.
 - Merge of data from difference files, including .csv, .JSON, and .jpg.
 
 ## Social Impact
-The multimodal dataset of tuberculosis patients, meticulously curated from the larger MultiCaRe Dataset, stands to have a significant social impact, particularly in the field of public health and medical research. Tuberculosis (TB) remains a major global health issue, especially in low- and middle-income countries, and the integration of CT imaging with clinical case reports in this dataset provides a rich resource for advanced diagnostic and treatment research. By facilitating the development of more precise algorithms for CT image segmentation and classification, as well as enhancing natural language processing (NLP) techniques for extracting medical terms from clinical notes, this dataset has the potential to improve the accuracy and efficiency of TB diagnosis.
+The multimodal dataset of tuberculosis patients, meticulously curated from the larger MultiCaRe Dataset, stands to have a significant social impact, particularly in the field of public health and medical research. Tuberculosis (TB) remains a major global health issue, especially in low- and middle-income countries, and the integration of CT imaging with clinical case reports in this dataset provides a rich resource for advanced diagnostic and treatment research. By facilitating the development of more precise algorithms for CT image segmentation and classification, as well as enhancing natural language processing (NLP) techniques for extracting medical terms from clinical notes, this dataset has the potential to improve the accuracy and efficiency of TB diagnosis.
+
+## Limitations
+
+## Citation
+**BibTeX:**
+```bibtex
+@dataset{NievasOffidani2023MultiCaRe,
+  author       = {Nievas Offidani, M. and Delrieux, C.},
+  title        = {The MultiCaRe Dataset: A Multimodal Case Report Dataset with Clinical Cases, Labeled Images and Captions from Open Access PMC Articles},
+  year         = {2023},
+  version      = {1.0},
+  publisher    = {Zenodo},
+  doi          = {10.5281/zenodo.10079370},
+  url          = {https://doi.org/10.5281/zenodo.10079370},
+}
+```