Biomedical　image　segmentation　and　classification　are　two　critical　components　in　computer-aided　diagnosis　systems.　However,　various　deep　convolutional　neural　networks　are　trained　by　a　single　task,　ignoring　the　potential　contribution　of　mutually　performing　multiple　tasks.　In　this　paper,　we　propose　an　unsupervised-guided　network　for　automated　white　blood　cell　(WBC)　and　skin　lesion　segmentation　and　classification　called　UG-Net.　UG-Net　consists　of　an　unsupervised-based　strategy　(US)　module,　an　enhanced　segmentation　network,　and　a　mask-guided　classification　network.　On　the　one　hand,　the　proposed　US　module　produces　coarse　masks　that　provide　a　prior　localization　map　for　the　proposed　segmentation　network　to　enhance　it　in　locating　and　segmenting　a　target　object　accurately.　On　the　other　hand,　the　enhanced　coarse　masks　predicted　by　the　proposed　segmentation　network　are　then　fed　into　the　proposed　classification　network　for　accurate　classification.　Moreover,　a　novel　contextual　encoding　module　is　presented　to　capture　high-level　information　and　preserve　spatial　information.　Meanwhile,　a　hybrid　loss　is　defined　to　alleviate　the　imbalance　training　problem.　Experimental　results　show　that　our　approach　achieves　state-of-the-art　segmentation　performance　on　two　public　biomedical　image　datasets.