A Lung Nodule Dataset with Histopathology-based Cancer Type Annotation
This addresses a data scarcity problem for medical AI developers and radiologists, enabling finer categorization of lung diseases for precise treatment, but it is incremental as it focuses on dataset creation rather than novel methods.
The researchers tackled the lack of publicly available datasets with expert-level cancer type annotations for lung nodules by curating a dataset of 330 annotated nodules from 95 patients, demonstrating its feasibility through evaluation with classical models.
Recently, Computer-Aided Diagnosis (CAD) systems have emerged as indispensable tools in clinical diagnostic workflows, significantly alleviating the burden on radiologists. Nevertheless, despite their integration into clinical settings, CAD systems encounter limitations. Specifically, while CAD systems can achieve high performance in the detection of lung nodules, they face challenges in accurately predicting multiple cancer types. This limitation can be attributed to the scarcity of publicly available datasets annotated with expert-level cancer type information. This research aims to bridge this gap by providing publicly accessible datasets and reliable tools for medical diagnosis, facilitating a finer categorization of different types of lung diseases so as to offer precise treatment recommendations. To achieve this objective, we curated a diverse dataset of lung Computed Tomography (CT) images, comprising 330 annotated nodules (nodules are labeled as bounding boxes) from 95 distinct patients. The quality of the dataset was evaluated using a variety of classical classification and detection models, and these promising results demonstrate that the dataset has a feasible application and further facilitate intelligent auxiliary diagnosis.