PMID- 33600311
OWN - NLM
STAT- MEDLINE
DCOM- 20211021
LR  - 20211021
IS  - 1558-254X (Electronic)
IS  - 0278-0062 (Linking)
VI  - 40
IP  - 10
DP  - 2021 Oct
TI  - Learning Inductive Attention Guidance for Partially Supervised Pancreatic Ductal 
      Adenocarcinoma Prediction.
PG  - 2723-2735
LID - 10.1109/TMI.2021.3060066 [doi]
AB  - Pancreatic ductal adenocarcinoma (PDAC) is the third most common cause of cancer 
      death in the United States. Predicting tumors like PDACs (including both 
      classification and segmentation) from medical images by deep learning is becoming 
      a growing trend, but usually a large number of annotated data are required for 
      training, which is very labor-intensive and time-consuming. In this paper, we 
      consider a partially supervised setting, where cheap image-level annotations are 
      provided for all the training data, and the costly per-voxel annotations are only 
      available for a subset of them. We propose an Inductive Attention Guidance 
      Network (IAG-Net) to jointly learn a global image-level classifier for 
      normal/PDAC classification and a local voxel-level classifier for semi-supervised 
      PDAC segmentation. We instantiate both the global and the local classifiers by 
      multiple instance learning (MIL), where the attention guidance, indicating 
      roughly where the PDAC regions are, is the key to bridging them: For global MIL 
      based normal/PDAC classification, attention serves as a weight for each instance 
      (voxel) during MIL pooling, which eliminates the distraction from the background; 
      For local MIL based semi-supervised PDAC segmentation, the attention guidance is 
      inductive, which not only provides bag-level pseudo-labels to training data 
      without per-voxel annotations for MIL training, but also acts as a proxy of an 
      instance-level classifier. Experimental results show that our IAG-Net boosts PDAC 
      segmentation accuracy by more than 5% compared with the state-of-the-arts.
FAU - Wang, Yan
AU  - Wang Y
FAU - Tang, Peng
AU  - Tang P
FAU - Zhou, Yuyin
AU  - Zhou Y
FAU - Shen, Wei
AU  - Shen W
FAU - Fishman, Elliot K
AU  - Fishman EK
FAU - Yuille, Alan L
AU  - Yuille AL
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20210930
PL  - United States
TA  - IEEE Trans Med Imaging
JT  - IEEE transactions on medical imaging
JID - 8310780
SB  - IM
MH  - *Adenocarcinoma
MH  - Attention
MH  - Humans
MH  - *Pancreatic Neoplasms/diagnostic imaging
MH  - Supervised Machine Learning
EDAT- 2021/02/19 06:00
MHDA- 2023/02/25 06:00
CRDT- 2021/02/18 17:12
PHST- 2021/02/19 06:00 [pubmed]
PHST- 2023/02/25 06:00 [medline]
PHST- 2021/02/18 17:12 [entrez]
AID - 10.1109/TMI.2021.3060066 [doi]
PST - ppublish
SO  - IEEE Trans Med Imaging. 2021 Oct;40(10):2723-2735. doi: 10.1109/TMI.2021.3060066. 
      Epub 2021 Sep 30.