Cancer region detection and subtype classification are two key tasks in digital pathology, but both are constrained by limited precise annotations on whole-slide images. This work proposes a semi-supervised multi-task framework that jointly learns detection and subtyping instead of training them as isolated steps. By coupling the two tasks under weak supervision, it reduces annotation demand and improves slide-level cancer classification.