International journal of computer assisted radiology and surgery · 2026
GUIDE-US: grade-informed unpaired distillation of encoder knowledge from histopathology to micro-ultrasound.
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Elghareb Tarek, Willis Emma, Wilson Paul F R, To Minh Nguyen Nhat, Abootorabi Mohammad Mahdi, Jamzad Amoon, Wodlinger Brian, Mousavi Parvin, Abolmaesumi Purang
Аннотация
PURPOSE: Non-invasive grading of prostate cancer (PCa) from micro-ultrasound (micro-US) could expedite triage and guide biopsies toward the most aggressive regions, yet current models struggle to infer tissue micro-structure at coarse imaging resolutions.
METHODS: We introduce an unpaired histopathology knowledge-distillation strategy that trains a micro-US encoder to emulate the embedding distribution of a pretrained histopathology foundation model, conditioned on International Society of Urological Pathology (ISUP) grades. Training requires no patient-level pairing or image registration, and histopathology inputs are not used at inference.
RESULTS: Compared to the current state of the art, our approach increases sensitivity to clinically significant PCa (csPCa) at 60% specificity by 3.5% and improves overall sensitivity at 60% specificity by 1.2%.
CONCLUSION: By enabling earlier and more dependable cancer risk stratification solely from imaging, our method advances clinical feasibility. Source code is available at https://github.com/DeepRCL/GUIDE-US.
METHODS: We introduce an unpaired histopathology knowledge-distillation strategy that trains a micro-US encoder to emulate the embedding distribution of a pretrained histopathology foundation model, conditioned on International Society of Urological Pathology (ISUP) grades. Training requires no patient-level pairing or image registration, and histopathology inputs are not used at inference.
RESULTS: Compared to the current state of the art, our approach increases sensitivity to clinically significant PCa (csPCa) at 60% specificity by 3.5% and improves overall sensitivity at 60% specificity by 1.2%.
CONCLUSION: By enabling earlier and more dependable cancer risk stratification solely from imaging, our method advances clinical feasibility. Source code is available at https://github.com/DeepRCL/GUIDE-US.