Constrained Deep Weak Supervision for Histopathology Image Segmentation

  title={Constrained Deep Weak Supervision for Histopathology Image Segmentation},
  author={Zhipeng Jia and Xingyi Huang and Eric I-Chao Chang and Yan Xu},
  journal={IEEE Transactions on Medical Imaging},
In this paper, we develop a new weakly supervised learning algorithm to learn to segment cancerous regions in histopathology images. [] Key Method The contributions of our algorithm are threefold: 1) we build an end-to-end learning system that segments cancerous regions with fully convolutional networks (FCNs) in which image-to-image weakly-supervised learning is performed; 2) we develop a DWS formulation to exploit multi-scale learning under weak supervision within FCNs; and 3) constraints about positive…

Transformer based multiple instance learning for weakly supervised histopathology image segmentation

A novel weakly supervised method for pixel-level segmentation in histopathology images is proposed, which introduces Transformer into the MIL framework to capture global or long-range dependencies and solves the shortcoming that instances are independent of each other in MIL.

Weakly Supervised Histopathology Image Segmentation With Sparse Point Annotations

A novel end-to-end weakly supervised learning framework named WESUP, trained by very sparse point annotations, that performs accurate segmentation and exhibits good generalizability in histopathology images and can even beat an advanced fully supervised segmentation network.

Weakly supervised histopathology image segmentation with self-attention.

A Regional Multiple Instance Learning Network for Whole Slide Image Segmentation

An end-to-end multiple instance learning (MIL)-based network for WSI segmentation using coarse-grained labels only and a novel regional MIL aggregator is proposed, which is used to identify the key instances and address the problem of data imbalance.

Strategies to Reduce the Expert Supervision Required for Deep Learning-Based Segmentation of Histopathological Images

This review identifies and describes four different approaches: the use of immunohistochemical markers as labels, realistic data augmentation, Generative Adversarial Networks (GAN), and transfer learning, which could help provide the very large number of annotated images needed to automate the segmentation of histological images using deep learning.

HistoSegResT: A Weakly Supervised Learning Method for Histopathology Image Segmentation

A weakly supervised learning method HistoSegResT (HSRT), which only uses image-level labels (i.e., malignant and benign) to complete histopathology image segmentation and can effectively relieve under-activation and over-activation of generated CAMs.

Weakly supervised histopathology cancer image segmentation and classification

Contexts-Constrained Multiple Instance Learning for Histopathology Image Analysis

A new algorithm along the line of weakly supervised learning is proposed; context constraints as a prior for multiple instance learning (ccMIL) is introduced, which significantly reduces the ambiguity in weak supervision (a 20% gain).

Constrained Convolutional Neural Networks for Weakly Supervised Segmentation

This work proposes Constrained CNN (CCNN), a method which uses a novel loss function to optimize for any set of linear constraints on the output space of a CNN, and demonstrates the generality of this new learning framework.

Weakly Supervised Cascaded Convolutional Networks

This work introduces two new architecture of cascaded networks, with either two cascade stages or three which are trained in an end-to-end pipeline to learn a convolutional neural network (CNN) under such conditions.

DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation

An efficient deep contour-aware network (DCAN) to solve this challenging problem under a unified multi-task learning framework and can be efficient when applied to large-scale histopathological data without resorting to additional steps to generate contours based on low-level cues for post-separating.

Weakly-and Semi-Supervised Learning of a Deep Convolutional Network for Semantic Image Segmentation

Expectation-Maximization (EM) methods for semantic image segmentation model training under weakly supervised and semi-supervised settings are developed and extensive experimental evaluation shows that the proposed techniques can learn models delivering competitive results on the challenging PASCAL VOC 2012 image segmentsation benchmark, while requiring significantly less annotation effort.

Deep convolutional activation features for large scale Brain Tumor histopathology image classification and segmentation

We propose a simple, efficient and effective method using deep convolutional activation features (CNNs) to achieve stat- of-the-art classification and segmentation for the MICCAI 2014 Brain Tumor

U-Net: Convolutional Networks for Biomedical Image Segmentation

It is shown that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.

Deeply-Supervised Nets

The proposed deeply-supervised nets (DSN) method simultaneously minimizes classification error while making the learning process of hidden layers direct and transparent, and extends techniques from stochastic gradient methods to analyze the algorithm.

Fully Convolutional Multi-Class Multiple Instance Learning

This work proposes a novel MIL formulation of multi-class semantic segmentation learning by a fully convolutional network that exploits the further supervision given by images with multiple labels.