A comparative study of zero-shot inference with large language models and supervised modeling in breast cancer pathology classification.
Madhumita SushilTravis ZackDivneet MandairZhiwei ZhengAhmed WaliYan-Ning YuYuwei QuanAtul J. ButtePublished in: CoRR (2024)
Keyphrases
- breast cancer
- language model
- breast cancer diagnosis
- bladder cancer
- mammogram images
- language modeling
- survival analysis
- computer aided detection
- n gram
- microcalcification clusters
- diagnosis of breast cancer
- logistic regression
- benign and malignant
- computer aided diagnosis
- machine learning
- early detection
- cancer datasets
- retrieval model
- feature selection
- supervised learning
- information retrieval
- probabilistic model
- unsupervised learning
- support vector
- breast cancer patients
- pattern recognition
- query expansion
- breast tissue
- test collection
- computer aided
- language models for information retrieval
- outcome prediction
- classification accuracy
- feature extraction
- cancer patients
- smoothing methods
- cad systems
- text classification
- active learning
- image analysis
- correct classification
- lung nodules
- semi supervised
- bayesian networks
- model selection
- class labels