Automated renal cell carcinoma detection and subtyping is limited by the lack of large whole-slide datasets with precise annotations. This paper proposes a semi-supervised framework built on a minimal point-based annotation strategy, where annotators only mark a few cancerous and non-cancerous points in each slide. The resulting detector and subtype classifier achieve performance comparable to much more heavily annotated alternatives while substantially lowering labeling effort.