Medical Image Analysis

1. Medical‑Image Segmentation and Classification

Medical‑image segmentation and classification are core tasks in medical‑image processing. Segmentation aims to precisely localize anatomical structures or lesion regions, while classification identifies corresponding pathological types. Conventional segmentation approaches include thresholding, region growing and active contour models; classification traditionally relies on hand‑crafted feature extraction together with machine‑learning classifiers such as SVM and Random Forest. With advances in deep learning, U‑Net and its variants achieve competitive segmentation performance via encoder‑decoder architectures and skip connections. Classification tasks adopt Convolutional Neural Networks (CNNs), transfer learning and Vision Transformers (ViT). Data augmentation and weak‑supervision strategies alleviate medical‑data scarcity. Current research focuses on multi‑modal fusion, 3D segmentation networks and end‑to‑end joint segmentation‑classification frameworks to enhance accuracy and robustness for computer‑aided clinical diagnosis. Key open challenges include limited annotated samples, class imbalance and model interpretability.

2. Learning under Class Imbalance

Class‑imbalance learning in medical‑image processing describes datasets where sample counts differ drastically between categories (e.g., normal versus pathological cases). This imbalance biases model training toward majority classes and severely degrades segmentation and classification performance, primarily caused by scarce samples for rare diseases or early‑stage lesions. Conventional solutions include over‑sampling, under‑sampling and cost‑sensitive learning (reweighting loss functions). However, high dimensionality of medical images may introduce noise from over‑sampling or loss of information from under‑sampling. Deep‑learning‑based strategies such as Focal Loss, GAN‑driven data augmentation, contrastive learning and curriculum learning are widely explored to strengthen model capture of features from minority classes. Transfer learning, meta‑learning and self‑supervised pre‑training further ease data scarcity. Major challenges involve balancing realism of synthetic data, model generalization and intra‑class variance under limited annotated samples, as well as optimizing sample utilization with domain knowledge.