AAI_2025_Capstone_Chronicles_Combined

Fig. 8 Classification report for Single Tasks Combination

Model Challenges and Unexpected Findings During early experimentation, we encountered a surprising failure mode where the EfficientNet model predicted all-zero outputs across most batches. This behavior persisted for multiple epochs and produced deceptively high binary accuracy (~81%), due to the imbalance in label distribution, many X-rays had no findings or only one active label. We interpreted this as the model falling into a local minimum, optimizing too aggressively for the majority class by minimizing false positives. To overcome this, we introduced a warm-up phase using binary cross-entropy with label smoothing, which effectively nudged the model out of its lazy baseline. Once we transitioned to focal loss, the model began making confident, non-zero predictions that aligned more closely with true pathology distributions.

Clinical Implications and Application​ ​

Both models—EfficientNet and the hybrid CNN—showed clear potential for clinical use as decision-support tools. Their ability to flag likely positive radiographs with high recall means they could function as triage assistants, helping radiologists prioritize studies more efficiently. In

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