University of Pittsburgh

Box-Adapt: Domain-Adaptive Medical ImageSegmentation using Bounding Box Supervision

Graduate Student
Friday, March 12, 2021 - 12:30pm - 1:00pm

Deep  learning  has  achieved  remarkable  success  in  medicalimage  segmentation,  but  it  usually  requires  a  large  number  of  imageslabeled  with  fine-grained  segmentation  masks,  and  the  annotation  ofthese masks can be very expensive and time-consuming. Therefore, recentmethods try to use unsupervised domain adaptation (UDA) methods toborrow  information  from  labeled  data  from  other  datasets  (source  do-mains) to a new dataset (target domain). However, due to the absenceof labels in the target domain, the performance of UDA methods is muchworse than that of the fully supervised method. In this paper, we pro-pose  a  weakly  supervised  domain  adaptation  setting,  in  which  we  canpartially label new datasets with bounding boxes, which are easier andcheaper  to  obtain  than  segmentation  masks.  Accordingly,  we  proposea new weakly-supervised domain adaptation method called Box-Adapt,which fully explores the fine-grained segmentation mask in the source do-main and the weak bounding box in the target domain. Our Box-Adaptis a two-stage method that first performs joint training on the source andtarget domains, and then conducts self-training with the pseudo-labelsof the target domain. We demonstrate the effectiveness of our method inthe liver segmentation task.

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