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Cluster-Based Learning from Weakly Labeled Bags in Digital Pathology

arXiv.org Machine Learning

To alleviate the burden of gathering detailed expert annotations when training deep neural networks, we propose a weakly supervised learning approach to recognize metastases in microscopic images of breast lymph nodes. We describe an alternative training loss which clusters weakly labeled bags in latent space to inform relevance of patch-instances during training of a convolutional neural network. We evaluate our method on the Camelyon dataset which contains high-resolution digital slides of breast lymph nodes, where labels are provided at the image-level and only subsets of patches are made available during training.


Monte-Carlo Sampling applied to Multiple Instance Learning for Histological Image Classification

arXiv.org Machine Learning

We propose a patch sampling strategy based on a sequential Monte-Carlo method for high resolution image classification in the context of Multiple Instance Learning. When compared with grid sampling and uniform sampling techniques, it achieves higher generalization performance. We validate the strategy on two artificial datasets and two histological datasets for breast cancer and sun exposure classification.


Classification and Disease Localization in Histopathology Using Only Global Labels: A Weakly-Supervised Approach

arXiv.org Machine Learning

Analysis of histopathology slides is a critical step for many diagnoses, and in particular in oncology where it defines the gold standard. In the case of digital histopathological analysis, highly trained pathologists must review vast whole-slide-images of extreme digital resolution ($100,000^2$ pixels) across multiple zoom levels in order to locate abnormal regions of cells, or in some cases single cells, out of millions. The application of deep learning to this problem is hampered not only by small sample sizes, as typical datasets contain only a few hundred samples, but also by the generation of ground-truth localized annotations for training interpretable classification and segmentation models. We propose a method for disease localization in the context of weakly supervised learning, where only image-level labels are available during training. Even without pixel-level annotations, we are able to demonstrate performance comparable with models trained with strong annotations on the Camelyon-16 lymph node metastases detection challenge. We accomplish this through the use of pre-trained deep convolutional networks, feature embedding, as well as learning via top instances and negative evidence, a multiple instance learning technique from the field of semantic segmentation and object detection.


Distill-to-Label: Weakly Supervised Instance Labeling Using Knowledge Distillation

arXiv.org Machine Learning

--Weakly supervised instance labeling using only image-level labels, in lieu of expensive fine-grained pixel annotations, is crucial in several applications including medical image analysis. In contrast to conventional instance segmentation scenarios in computer vision, the problems that we consider are characterized by a small number of training images and non-local patterns that lead to the diagnosis. In this paper, we explore the use of multiple instance learning (MIL) to design an instance label generator under this weakly supervised setting. Motivated by the observation that an MIL model can handle bags of varying sizes, we propose to repurpose an MIL model originally trained for bag-level classification to produce reliable predictions for single instances, i.e., bags of size 1 . T o this end, we introduce a novel regularization strategy based on virtual adversarial training for improving MIL training, and subsequently develop a knowledge distillation technique for repurposing the trained MIL model. Using empirical studies on colon cancer and breast cancer detection from histopathological images, we show that the proposed approach produces high-quality instance-level prediction and significantly outperforms state-of-the MIL methods.


Learning Permutation Invariant Representations using Memory Networks

arXiv.org Machine Learning

Many real world tasks such as 3D object detection and high-resolution image classification involve learning from a set of instances. In these cases, only a group of instances, a set, collectively contains meaningful information and therefore only the sets have labels, and not individual data instances. In this work, we present a permutation invariant neural network called a \textbf{Memory-based Exchangeable Model (MEM)} for learning set functions. The model consists of memory units that embed an input sequence to high-level features (memories) enabling the model to learn inter-dependencies among instances of the set in the form of attention vectors. To demonstrate its learning ability, we evaluated our model on test datasets created using MNIST, point cloud classification, and population estimation. We also tested the model for classifying histopathology whole slide images to discriminate between two subtypes of Lung cancer---Lung Adenocarcinoma, and Lung Squamous Cell Carcinoma. We systematically extracted patches from lung cancer images from The Cancer Genome Atlas~(TCGA) dataset, the largest public repository of histopathology images. The proposed method achieved a competitive classification accuracy of 84.84\%. The results on other datasets are promising and demonstrate the efficacy of our model.