CNN with Multiple Instance Learning for Microscopy Image Classification
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Solution Overview
Problem
Current automated image analysis systems for microscopy are inadequate for classifying and segmenting microscopy images, particularly for non-mammalian cells like yeast and Caenorhabditis elegans, due to the lack of large datasets labeled at the single cell level and the expense of generating dense pixel-level ground truth labels.
Innovation Solution
A convolutional neural network (CNN) with a global multiple instance learning (MIL) pooling layer is used to classify and segment microscopy images, allowing for the classification of cell classes without the need for segmentation or single cell labeling, using whole image level annotations and robust pooling functions like Noisy-AND to handle outliers and variable cell densities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dense pixel-level ground truth labels are used for training, then segmentation precision is improved, but the cost and time for data preparation increases significantly
Solution Approach 1:
The patent applies partial labeling by providing ground truth annotations for only a subset of images rather than all training images. The MIL framework learns from these partially labeled images and generalizes to unlabeled images, reducing annotation effort while maintaining segmentation performance. This directly addresses the contradiction by performing segmentation with less than complete labeling.
Solution Approach 2:
The patent introduces a multiple instance learning framework as an intermediary between fully labeled and completely unlabeled data. The MIL pooling layer acts as a mediator that aggregates features from multiple instances (cells) in an image to produce image-level predictions, enabling the system to learn from weak labels and propagate annotations to unlabeled images through the learned feature representations.
2Adaptability or versatility
If traditional hand-tuned analysis pipelines are used, then adaptability to different organisms is improved, but automation and throughput are reduced
Solution Approach 1:
The patent creates a universal deep learning framework that can process microscopy images from multiple organisms (mammalian cells, yeast, C. elegans) using the same architecture. The CNN learns organism-agnostic features from the images, and the MIL framework handles variable numbers of cells and arbitrary image sizes, making the system adaptable to different organisms without requiring organism-specific hand-tuned parameters for each case.
Solution Approach 2:
The system performs self-service by automatically learning feature representations and segmentation parameters from the data without requiring manual hand-tuning for each organism or assay type. The end-to-end training allows the network to automatically adapt to different organisms and experimental conditions, eliminating the need for researchers to manually adjust segmentation and classification parameters for each new organism.
3Measurement precision
If fully convolutional neural networks are used for segmentation, then segmentation capability is improved, but the ability to classify populations of objects in arbitrary size images is reduced
Solution Approach 1:
The patent divides the image into multiple instances or cells, where each cell is treated as a separate instance that can be independently processed by the CNN. The MIL pooling layer then aggregates features from these segmented instances to produce image-level classification predictions. This segmentation approach enables the system to handle arbitrary image sizes and variable numbers of cells while maintaining both segmentation capability and population classification ability.
Solution Approach 2:
The patent transitions from pixel-level segmentation to instance-level representation by mapping pixel features to cell-level features through the MIL framework. This dimensional transformation allows the system to work with images of arbitrary size by representing them as collections of instances rather than fixed-size pixel grids, enabling population classification across variable image dimensions.
Data Source
AI summary
Systems and methods that receive as input microscopy images, extract features, and apply layers of processing units to compute one or more set of cellular phenotype features, corresponding to cellular densities and/or fluorescence measured under different conditions. The system is a neural network architecture having a convolutional neural network followed by a multiple instance learning (MIL) pooling layer. The system does not necessarily require any segmentation steps or per cell labels as the convolutional neural network can be trained and tested directly on raw microscopy images in real-time. The system computes class specific feature maps for every phenotype variable using a fully convolutional neural network and uses multiple instance learning to aggregate across these class specific feature maps. The system produces predictions for one or more reference cellular phenotype variables based on microscopy images with populations of cells.