Because　the　COVID-19　virus　is　highly　transmissible,　leading　to　a　worldwide　increment　of　new　infections　and　deaths　daily,　the　development　of　an　automated　tool　to　identify　COVID-19　using　CT　images　has　attracted　much　attention.　Significantly,　deep　metric　learning　can　be　deployed　to　cluster　and　classify　the　fine-grained　CT　images,　which　aims　to　learn　a　mapping　from　the　original　objects　to　a　discriminative　feature　embedding　space.　Previous　deep　metric　learning　works　have　been　proposed　to　construct　various　structures　of　loss,　mine　hard　samples,　or　introduce　regularization　constraints,　etc.　In　general,　traditional　loss　functions　of　deep　metric　learning　methods　are　based　on　constraining　the　distance　of　the　triplet　embeddings　in　the　feature　space.　Instead　of　focusing　on　the　previous　research　directions,　in　this　work,　we　pay　attention　to　exploring　confusing　triplet　embeddings,　for　the　reason　that　confusing　triplet　embeddings　perform　a　side　effect　on　the　majority　of　deep　triplet-based　metric　learning　methods.　By　considering　the　spatial　relation　of　triplet　embedding,　and　conducting　theoretical　analysis　in　the　feature　space,　we　propose　an　approach　to　recognize　the　confusing　triplet　embeddings　and　construct　a　Confusing　Triplet　Embedding　Learning　(CTEL)　method　by　adding　a　hard　constraint　on　the　confusing　triplet　embeddings.　The　extensive　experiments　indicate　that　our　proposed　CTEL　method　achieves　more　excellent　performance　on　two　medical　CT　image　datasets　and　two　fine-grained　standard　image　datasets　compared　with　many　state-of-the-art　methods.　©　ICML　2022.