Timely　detection　of　hard　exudates　in　fundus　images　can　effectively　avoid　the　severity　of　the　disease,　but　the　labelling　of　small　and　numerous　lesion　areas　requires　a　lot　of　labour　costs.　This　paper　proposes　a　semi-supervised　segmentation　network,　which　integrates　pseudo-labels　and　multi-level　features　consistency　correction.　It　achieves　accurate　segmentation　of　hard　exudates　by　making　full　use　of　a　small　amount　of　labelled　data　and　a　large　amount　of　unlabelled　data.　The　network　effectively　extracts　features　from　the　unlabelled　data　through　knowledge　transfer　of　the　teacher-student　model,　and　incorporates　a　Transformer　network　for　auxiliary　training　to　promote　the　quality　of　transfer.　In　addition,　three　unsupervised　losses　are　introduced　to　improve　the　performance:　the　perturbation　loss　improves　the　robustness　of　the　model　to　noise　by　adding　different　noises　to　the　same　input;　the　multi-level　feature　consistency　correction　loss　ensures　the　consistency　of　features　of　the　student　model　at　different　scales;　and　the　pseudo-labelling　cross-supervision　loss　utilizes　the　generated　pseudo-labels　for　supervision　between　CNN　and　Transformer.　By　comparing　the　segmentation　results　with　different　proportion　of　the　labelled　data,　it　has　better　segmentation　performance　compared　to　other　methods.　The　proposed　methods　can　totally　increase　dice　by　16.56%　and　mean　intersection　over　union　(MIoU)　by　25.11%.　©　2024　The　Author(s).　IET　Image　Processing　published　by　John　Wiley　&　Sons　Ltd　on　behalf　of　The　Institution　of　Engineering　and　Technology.