Learning Inductive Attention Guidance for Partially Supervised
Pancreatic Ductal Adenocarcinoma Prediction
- URL: http://arxiv.org/abs/2105.14773v1
- Date: Mon, 31 May 2021 08:16:09 GMT
- Title: Learning Inductive Attention Guidance for Partially Supervised
Pancreatic Ductal Adenocarcinoma Prediction
- Authors: Yan Wang, Peng Tang, Yuyin Zhou, Wei Shen, Elliot K. Fishman, and Alan
L. Yuille
- Abstract summary: Pancreatic ductal adenocarcinoma (PDAC) is the third most common cause of cancer death in the United States.
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.
- Score: 73.96902906734522
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
Related papers
- ConDiSR: Contrastive Disentanglement and Style Regularization for Single Domain Generalization [42.810247034149214]
Medical data often exhibits distribution shifts, which cause test-time performance degradation for deep learning models trained using standard pipelines.
This study highlights the importance and challenges of exploring Single Domain Generalization frameworks in the context of the classification task.
arXiv Detail & Related papers (2024-03-14T13:50:44Z) - Cross-supervised Dual Classifiers for Semi-supervised Medical Image
Segmentation [10.18427897663732]
Semi-supervised medical image segmentation offers a promising solution for large-scale medical image analysis.
This paper proposes a cross-supervised learning framework based on dual classifiers (DC-Net)
Experiments on LA and Pancreas-CT dataset illustrate that DC-Net outperforms other state-of-the-art methods for semi-supervised segmentation.
arXiv Detail & Related papers (2023-05-25T16:23:39Z) - Uncertainty Driven Bottleneck Attention U-net for Organ at Risk
Segmentation [20.865775626533434]
Organ at risk (OAR) segmentation in computed tomography (CT) imagery is a difficult task for automated segmentation methods.
We propose a multiple decoder U-net architecture and use the segmentation disagreement between the decoders as attention to the bottleneck of the network.
For accurate segmentation, we also proposed a CT intensity integrated regularization loss.
arXiv Detail & Related papers (2023-03-19T23:45:32Z) - Prior Knowledge-Guided Attention in Self-Supervised Vision Transformers [79.60022233109397]
We present spatial prior attention (SPAN), a framework that takes advantage of consistent spatial and semantic structure in unlabeled image datasets.
SPAN operates by regularizing attention masks from separate transformer heads to follow various priors over semantic regions.
We find that the resulting attention masks are more interpretable than those derived from domain-agnostic pretraining.
arXiv Detail & Related papers (2022-09-07T02:30:36Z) - PCA: Semi-supervised Segmentation with Patch Confidence Adversarial
Training [52.895952593202054]
We propose a new semi-supervised adversarial method called Patch Confidence Adrial Training (PCA) for medical image segmentation.
PCA learns the pixel structure and context information in each patch to get enough gradient feedback, which aids the discriminator in convergent to an optimal state.
Our method outperforms the state-of-the-art semi-supervised methods, which demonstrates its effectiveness for medical image segmentation.
arXiv Detail & Related papers (2022-07-24T07:45:47Z) - Improving Classification Model Performance on Chest X-Rays through Lung
Segmentation [63.45024974079371]
We propose a deep learning approach to enhance abnormal chest x-ray (CXR) identification performance through segmentations.
Our approach is designed in a cascaded manner and incorporates two modules: a deep neural network with criss-cross attention modules (XLSor) for localizing lung region in CXR images and a CXR classification model with a backbone of a self-supervised momentum contrast (MoCo) model pre-trained on large-scale CXR data sets.
arXiv Detail & Related papers (2022-02-22T15:24:06Z) - Cross-Modality Brain Tumor Segmentation via Bidirectional
Global-to-Local Unsupervised Domain Adaptation [61.01704175938995]
In this paper, we propose a novel Bidirectional Global-to-Local (BiGL) adaptation framework under a UDA scheme.
Specifically, a bidirectional image synthesis and segmentation module is proposed to segment the brain tumor.
The proposed method outperforms several state-of-the-art unsupervised domain adaptation methods by a large margin.
arXiv Detail & Related papers (2021-05-17T10:11:45Z) - Unsupervised Adversarial Domain Adaptation For Barrett's Segmentation [0.8602553195689513]
Automated segmentation can help clinical endoscopists to assess and treat Barrett's oesophagus (BE) area more accurately.
Supervised models require large amount of manual annotations incorporating all data variability in the training data.
In this work, we aim to alleviate this problem by applying an unsupervised domain adaptation technique (UDA)
Our results show that the UDA-based approach outperforms traditional supervised U-Net segmentation by nearly 10% on both Dice similarity coefficient and intersection-over-union.
arXiv Detail & Related papers (2020-12-09T20:59:25Z) - Weakly Supervised Deep Nuclei Segmentation Using Partial Points
Annotation in Histopathology Images [51.893494939675314]
We propose a novel weakly supervised segmentation framework based on partial points annotation.
We show that our method can achieve competitive performance compared to the fully supervised counterpart and the state-of-the-art methods.
arXiv Detail & Related papers (2020-07-10T15:41:29Z) - Localization of Critical Findings in Chest X-Ray without Local
Annotations Using Multi-Instance Learning [0.0]
deep learning models commonly suffer from a lack of explainability.
Deep learning models require locally annotated training data in form of pixel level labels or bounding box coordinates.
In this work, we address these shortcomings with an interpretable DL algorithm based on multi-instance learning.
arXiv Detail & Related papers (2020-01-23T21:29:14Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.