Image Classification in Healthcare: End-to-End Interview Task

Context: You are designing and evaluating an image-classification system for a healthcare application (e.g., chest X-ray, pathology tiles, or MRI slices). Address the following prompts concisely and rigorously.

(a) Overfitting: Definition, Diagnosis, and Remedies

• Define overfitting rigorously.
• Diagnose it using: learning curves, validation metrics, calibration, and error analysis.
• Propose targeted remedies (e.g., regularization, augmentation, ensembling) and justify trade-offs.

(b) DenseNet Deep Dive

• Explain DenseNet: connectivity pattern, growth rate k, bottleneck/transition layers.
• Compare parameter/memory complexity vs. ResNet and impact on gradient flow.
• When would you prefer DenseNet in practice?
• Define a small DenseNet configuration and compute its approximate parameter count.

(c) Data Preprocessing and Augmentation

• Propose a preprocessing/augmentation pipeline (normalization, resampling, contrast/denoise, artifact handling).
• Address label imbalance.
• List common data-leakage traps and how to detect/prevent them.

(d) Hyperparameters and Tuning Strategy

• Define model hyperparameters vs. learned parameters with examples.
• Propose a tuning strategy: search spaces, budgets, early stopping, and regularization choices.
• How will you make it reproducible?

(e) Patient-Level Cross-Validation and Fair Model Comparison

• Design a patient-level K-fold (or nested) cross-validation that prevents leakage across the same patient/scanner/time.
• Show how you aggregate metrics with confidence intervals and compare models fairly.