Compositional Latent Representation for Weakly Supervised Medical Image Segmentation
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Solution Overview
Problem
The high computational cost and time required for fully annotating medical image segmentation masks, especially in cardiac MRI data, limit the efficient training of deep learning models for accurate and automatic medical image processing.
Innovation Solution
A medical image processing apparatus that uses weak supervision annotation information to train a deep learning network with a compositional latent representation comprising von Mises Fisher kernels, reducing the need for extensive annotations by guiding the representation towards different anatomical, pathological, and medical device objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full annotation of segmentation masks is used for training deep learning models, then segmentation accuracy is improved, but annotation time and computational cost increase significantly
Solution Approach 1:
The patent applies partial supervision by using only bounding box annotations instead of complete segmentation masks for training. The weakly supervised learning approach processes only essential information (object locations via bounding boxes) rather than full detailed annotations, significantly reducing annotation time while still enabling the model to learn effective segmentation through compositional latent representations
Solution Approach 2:
The patent introduces compositional latent representations as an intermediary between bounding box annotations and final segmentation outputs. These latent representations serve as a bridge that translates weak supervision signals into meaningful segmentation information, allowing the model to achieve accurate segmentation without requiring direct full-mask annotations during training
2Measurement precision
If full annotation of segmentation masks is used for training deep learning models, then segmentation accuracy is improved, but computational cost increases significantly
Solution Approach 1:
The patent reduces computational cost by using partial annotation data (bounding boxes only) instead of complete segmentation masks. This partial supervision approach requires significantly less computational resources for both data preparation and model training, while the compositional latent representation framework efficiently processes this reduced information to achieve competitive segmentation performance
Solution Approach 2:
The patent extracts only the essential information needed for training (bounding box locations) and discards the computationally expensive full segmentation mask annotations. By taking out only the critical spatial information and using compositional latent representations to reconstruct segmentation details, the method reduces computational burden while maintaining segmentation accuracy
3Loss of time
If weak supervision annotation information is used for training, then annotation time and cost are reduced, but segmentation performance may deteriorate in low-data regimes
Solution Approach 1:
The compositional latent representations act as an intermediary that enhances the information content of weak supervision signals. By introducing this intermediate representation layer, the model can effectively learn from limited bounding box annotations and generalize better in low-data regimes, compensating for the reduced annotation quality with richer latent feature learning
Solution Approach 2:
The patent changes the parameter representation by using compositional latent representations with specific variance parameters (e.g., variance of 30) to model the uncertainty and variability in weakly supervised learning. This parameter adjustment allows the model to adapt to low-data regimes by controlling the confidence and flexibility of learned representations, improving reliability despite using weak annotations
Data Source
AI summary
A medical image processing apparatus includes a memory storing training medical images, each annotated with respective weak supervision annotation information relating to at least one object represented in the training medical image, the at least one object comprising an anatomical object, a pathology, or a medical device; and processing circuitry configured to use the plurality of training medical images to train a deep learning network to perform a task, wherein the training of the deep learning network includes training a compositional latent representation comprising a plurality of kernels, wherein the training of the compositional latent network includes using the weak supervision annotation information to provide weak supervision of the training of the computational latent representation, thereby guiding the compositional latent representation towards a representation in which different ones of the kernels are representative of different objects, the different objects comprising at least one anatomical object, pathology, or medical device.