
This study develops a few-shot learning model to effectively classify primary lung cancer and other metastatic cancers in cytological images obtained during endobronchial ultrasound (EBUS) procedures, enabling early detection and treatment planning for patients.


coremsg

classifying-primary-lung-cancer-and-metastatic-cancers-in-cytological-imaging-via-few-shot-learning-on-endobronchial-ultrasound-procedures


Classifying Primary Lung Cancer and Metastatic Cancers in Cytological Imaging via Few-Shot Learning on Endobronchial Ultrasound Procedures


title_rewrite


Directly training neural networks on raw signal data from medical imaging scanners could provide more nuanced and accurate insights compared to using pre-processed images, but faces significant technical and practical hurdles that need to be addressed.


leveraging-raw-signal-data-for-improved-deep-learning-in-medical-imaging-opportunities-and-challenges


Leveraging Raw Signal Data for Improved Deep Learning in Medical Imaging: Opportunities and Challenges
