Domain-Adaptive Instance Segmentation With Contrastive Flow Alignment
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Solution Overview
Problem
Existing neural network-based image segmentation models struggle with domain shift when applied to new data distributions, requiring costly and time-consuming retraining or large amounts of target data annotation, especially in tasks like cellular instance segmentation.
Innovation Solution
A method for domain adaptation using a pre-trained neural network model, adapted with a contrastive flow loss and contrastive mask loss components, allowing segmentation of new data distributions with only a few annotated samples, leveraging Cellpose as a base model and incorporating few-shot learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a pre-trained neural network model is used for segmentation, then segmentation accuracy is improved, but the model fails to adapt to new data distributions (domain shift)
Solution Approach 1:
The patent changes the parameters of the pre-trained model by introducing domain adaptation loss functions (contrastive loss, gradient flow alignment loss) that modify the model's behavior to accommodate new data distributions while preserving the learned segmentation capabilities from the source domain
Solution Approach 2:
The patent introduces an intermediary adaptation layer that bridges the source domain pre-trained model and the target domain data. This intermediary consists of domain adaptation loss functions that mediate the transfer of knowledge while adapting to new distributions
2Adaptability or versatility
If the model is retrained on target data, then adaptability to new data distributions is improved, but training time and resource consumption increase
Solution Approach 1:
Instead of complete retraining, the patent applies partial action by using only a few annotated target samples (few-shot learning) combined with domain adaptation loss functions to achieve sufficient adaptation, significantly reducing training time while maintaining effectiveness
Solution Approach 2:
The patent performs preliminary action by pre-training on a large source dataset first, then applying lightweight domain adaptation on the target domain. This preliminary pre-training establishes a strong baseline that requires minimal further adaptation
3Adaptability or versatility
If large amounts of target data are annotated, then adaptability to new data distributions is improved, but annotation cost and time increase
Solution Approach 1:
The patent uses partial action by requiring only a small subset of annotated target data (few-shot setting) rather than large amounts. The domain adaptation loss functions enable effective learning from this limited annotated data
Data Source
AI summary
System and methods for object segmentation include providing a pre-trained neural network model to segment object instances based on a first set of images and a first loss function. The neural network, for a pixel, can generate a gradient flow that points towards a center of an object structure and provides a probability score indicating a probability of the pixel belonging to the object structure. An adapted neural network model can be generated from the pre-trained neural network model to account for domain shifted new input images by training the pre-trained neural network model on a second set of images and a loss function that comprises a contrastive flow loss component and a contrastive mask loss component.