An　intraoperative　diagnosis　is　critical　for　precise　cancer　surgery.　However,　traditional　intraoperative　assessments　based　on　hematoxylin　and　eosin　(H&E)　histology,　such　as　frozen　section,　are　time-,　resource-,　and　labor-intensive,　and　involve　specimen-consuming　concerns.　Here,　we　report　a　near-realtime　automated　cancer　diagnosis　workflow　for　breast　cancer　that　combines　dynamic　full　-field　optical　coherence　tomography　(D-FFOCT),　a　label　-free　optical　imaging　method,　and　deep　learning　for　bedside　tumor　diagnosis　during　surgery.　To　classify　the　benign　and　malignant　breast　tissues,　we　conducted　a　prospective　cohort　trial.　In　the　modeling　group　(　n　=　182),　D-FFOCT　images　were　captured　from　April　26　to　June　20,　2018,　encompassing　48　benign　lesions,　114　invasive　ductal　carcinoma　(IDC),　10　invasive　lobular　carcinoma,　4　ductal　carcinoma　in　situ　(DCIS),　and　6　rare　tumors.　Deep　learning　model　was　built　up　and　fine-tuned　in　10,357　D-FFOCT　patches.　Subsequently,　from　June　22　to　August　17,　2018,　independent　tests　(　n　=　42)　were　conducted　on　10　benign　lesions,　29　IDC,　1　DCIS,　and　2　rare　tumors.　The　model　yielded　excellent　performance,　with　an　accuracy　of　97.62%,　sensitivity　of　96.88%　and　specificity　of　100%;　only　one　IDC　was　misclassified.　Meanwhile,　the　acquisition　of　the　D-FFOCT　images　was　nondestructive　and　did　not　require　any　tissue　preparation　or　staining　procedures.　In　the　simulated　intraoperative　margin　evaluation　procedure,　the　time　required　for　our　novel　workflow　(approximately　3　min)　was　significantly　shorter　than　that　required　for　traditional　procedures　(approximately　30　min).　These　findings　indicate　that　the　combination　of　D-FFOCT　and　deep　learning　algorithms　can　streamline　intraoperative　cancer　diagnosis　independently　of　traditional　pathology　laboratory　procedures.　(c)　2024　Science　China　Press.　Published　by　Elsevier　B.V.　and　Science　China　Press.