UiT_TILs - Replication Data for 'A Pragmatic Machine Learning Approach to Quantify Tumor Infiltrating Lymphocytes in Whole Slide Images'
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'This dataset can be used to replicate the findings in 'A Pragmatic Machine Learning Approach to Quantify Tumor Infiltrating Lymphocytes in Whole Slide Images'. The motivation for this paper is that increased levels of tumor infiltrating lymphocytes (TILs) indicate favorable outcomes in many types of cancer. Our aim is to leverage computational pathology to automatically quantify TILs in standard diagnostic whole-tissue hematoxylin and eosin stained section slides (H&E slides). Our approach is to transfer an open source machine learning method for segmentation and classification of nuclei in H&E slides trained on public data to TIL quantification without manual labeling of our data. Our results show that improved data augmentation improves immune cell detection in H&E WSIs. Moreover, the resulting TIL quantification correlates to pat' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["deep learning","DL","immune cell","machine learning","ML","non-small cell lung cancer","NSCLC","TIL","tissue infiltrating lymphycytes"]} <|eot_id|>
 Here is the Description of a dataset. Description:'This dataset can be used to replicate the findings in 'A Pragmatic Machine Learning Approach to Quantify Tumor Infiltrating Lymphocytes in Whole Slide Images'. The motivation for this paper is that increased levels of tumor infiltrating lymphocytes (TILs) indicate favorable outcomes in many types of cancer. Our aim is to leverage computational pathology to automatically quantify TILs in standard diagnostic whole-tissue hematoxylin and eosin stained section slides (H&E slides). Our approach is to transfer an open source machine learning method for segmentation and classification of nuclei in H&E slides trained on public data to TIL quantification without manual labeling of our data. Our results show that improved data augmentation improves immune cell detection in H&E WSIs. Moreover, the resulting TIL quantification correlates to pat' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
This dataset can be used to replicate the findings in 'A Pragmatic Machine Learning Approach to Quantify Tumor Infiltrating Lymphocytes in Whole Slide Images'. The motivation for this paper is that increased levels of tumor infiltrating lymphocytes (TILs) indicate favorable outcomes in many types of cancer. Our aim is to leverage computational pathology to automatically quantify TILs in standard diagnostic whole-tissue hematoxylin and eosin stained section slides (H&E slides). Our approach is to transfer an open source machine learning method for segmentation and classification of nuclei in H&E slides trained on public data to TIL quantification without manual labeling of our data. Our results show that improved data augmentation improves immune cell detection in H&E WSIs. Moreover, the resulting TIL quantification correlates to pat
deep learning; DL; immune cell; machine learning; ML; non-small cell lung cancer; NSCLC; TIL; tissue infiltrating lymphycytes