Disentangled representations of microscopy images
Jacopo Dapueto, Vito Paolo Pastore, Nicoletta Noceti, Francesca Odone
TL;DR
This paper introduces a Disentangled Representation Learning framework for microscopy image classification, improving interpretability while maintaining accuracy across diverse microscopic datasets.
Contribution
It proposes a novel DRL approach that transfers representations from synthetic data to enhance interpretability in microscopy image analysis.
Findings
Effective trade-off between accuracy and interpretability.
Successful application across three different microscopic domains.
Representation transfer from synthetic to real data improves model understanding.
Abstract
Microscopy image analysis is fundamental for different applications, from diagnosis to synthetic engineering and environmental monitoring. Modern acquisition systems have granted the possibility to acquire an escalating amount of images, requiring a consequent development of a large collection of deep learning-based automatic image analysis methods. Although deep neural networks have demonstrated great performance in this field, interpretability, an essential requirement for microscopy image analysis, remains an open challenge. This work proposes a Disentangled Representation Learning (DRL) methodology to enhance model interpretability for microscopy image classification. Exploiting benchmark datasets from three different microscopic image domains (plankton, yeast vacuoles, and human cells), we show how a DRL framework, based on transferring a representation learnt from synthetic…
Peer Reviews
Decision·Submitted to ICLR 2025
* The paper evaluates the recent ideas of disentangled representation learning using weak supervision in a more realistic application. * The paper also presents an alternative to learning the disentangled representation from RGB images based on models pretrained at large scale. * The paper proposes a new sprites dataset to facilitate the interpretation of microscopy images.
* The technical contribution is limited. Beyond the sprites dataset and the use of pretrained features, many of the ideas have been presented in previous works. * The experimental evaluation is limited to quantifying the impact of classifier types (GBT vs MLP) and input type (RGB vs DINO features). Many questions remain open regarding how much classification accuracy could be obtained without the proposed disentanglement procedure. Can the authors compare results of training a classifier directl
1. The manuscript is well-written and easy to follow, with clear organization and logical flow. 2. The application of weakly-supervised DRL to real-world image analysis represents a promising and valuable research direction.
1.The scope of this work appears too narrow, focusing solely on microscopy images. The proposed approach might be more convincing if demonstrated on natural images as well. 2.The authors fail to adequately justify why DRL should be specifically applied to microscopy image analysis. Furthermore, they do not clearly articulate whether this specific application domain poses new challenges or requirements for DRL that could lead to innovative solutions. The authors' insights into these aspects ar
The paper explores the application of an existing DRL framework to the specific domain of microscopy images. This idea is interesting as it shows a potential pathway for combining DRL with microscopy image analysis.
A significant weakness as it seems, is the absence of a comparison with other similar methods. The paper presents only one framework and does not discuss or evaluate alternative approaches, which weakens the case for this framework’s efficacy or advantage over existing methods. The contributions of the paper in terms of novelty are unclear. The study applies an existing DRL approach to a new domain but does not appear to introduce any fundamentally new concepts, techniques, or substantial modif
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsCell Image Analysis Techniques