AAI_2025_Capstone_Chronicles_Combined

attempting to automate diagnosis, our intent was to design a recall-focused support tool that flags potentially important findings for clinical review. Because missed diagnoses can have serious consequences, especially in fast-paced or resource-limited environments, we tuned the models to be deliberately overinclusive, prioritizing sensitivity over precision. We believe this approach allows the system to act as a second set of eyes, helping healthcare professionals avoid overlooking subtle or ambiguous abnormalities. Our dataset is sourced from the NIH ChestX-ray repository, which contains over 100,000 frontal-view chest radiographs labeled with up to 14 distinct thoracic pathologies (Wang et al., 2017). Each image is associated with patient metadata including patient ID, age, gender, and original image dimensions. The labels were generated through automated keyword extraction from radiology reports, which introduced some known noise into the dataset. The original dataset contains 14 diagnostic labels include Atelectasis, Cardiomegaly, Consolidation, Edema, Effusion, Emphysema, Fibrosis, Hernia, Infiltration, Mass, Nodule, Pleural Thickening, Pneumonia, and Pneumothorax. Additionally, the dataset also includes a 15th label, “No Finding,” used to indicate the absence of all other conditions. After inspecting the label distribution and performing exploratory data analysis (see Fig. 1–3), we identified major class imbalance as a significant challenge. “No Finding” accounted for nearly 40% of the dataset, while rarer conditions such as “Hernia” had fewer than 200 labeled images. To address this, we downsampled “No Finding” to 10,000 images to reduce its overwhelming influence on the model thereby preventing class imbalance. We also grouped the original 15 labels into 7 broader diagnostic categories based on clinical similarity. This label Dataset Summary

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