Deep Low-Shot Learning for Biological Image Classification and Visualization from Limited Training Samples

• URL: http://arxiv.org/abs/2010.10050v1
• Date: Tue, 20 Oct 2020 06:06:06 GMT
• Title: Deep Low-Shot Learning for Biological Image Classification and Visualization from Limited Training Samples
• Authors: Lei Cai and Zhengyang Wang and Rob Kulathinal and Sudhir Kumar and Shuiwang Ji
• Abstract summary: In situ hybridization (ISH) gene expression pattern images from the same developmental stage are compared. labeling training data with precise stages is very time-consuming even for biologists. We propose a deep two-step low-shot learning framework to accurately classify ISH images using limited training images.
• Score: 52.549928980694695
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Predictive modeling is useful but very challenging in biological image analysis due to the high cost of obtaining and labeling training data. For example, in the study of gene interaction and regulation in Drosophila embryogenesis, the analysis is most biologically meaningful when in situ hybridization (ISH) gene expression pattern images from the same developmental stage are compared. However, labeling training data with precise stages is very time-consuming even for evelopmental biologists. Thus, a critical challenge is how to build accurate computational models for precise developmental stage classification from limited training samples. In addition, identification and visualization of developmental landmarks are required to enable biologists to interpret prediction results and calibrate models. To address these challenges, we propose a deep two-step low-shot learning framework to accurately classify ISH images using limited training images. Specifically, to enable accurate model training on limited training samples, we formulate the task as a deep low-shot learning problem and develop a novel two-step learning approach, including data-level learning and feature-level learning. We use a deep residual network as our base model and achieve improved performance in the precise stage prediction task of ISH images. Furthermore, the deep model can be interpreted by computing saliency maps, which consist of pixel-wise contributions of an image to its prediction result. In our task, saliency maps are used to assist the identification and visualization of developmental landmarks. Our experimental results show that the proposed model can not only make accurate predictions, but also yield biologically meaningful interpretations. We anticipate our methods to be easily generalizable to other biological image classification tasks with small training datasets.

Related papers

• Scaling Laws of Synthetic Images for Model Training ... for Now [54.43596959598466]
  We study the scaling laws of synthetic images generated by state of the art text-to-image models. We observe that synthetic images demonstrate a scaling trend similar to, but slightly less effective than, real images in CLIP training.
  arXiv  Detail & Related papers  (2023-12-07T18:59:59Z)
• Nucleus-aware Self-supervised Pretraining Using Unpaired Image-to-image Translation for Histopathology Images [3.8391355786589805]
  We propose a novel nucleus-aware self-supervised pretraining framework for histopathology images. The framework aims to capture the nuclear morphology and distribution information through unpaired image-to-image translation. The experiments on 7 datasets show that the proposed pretraining method outperforms supervised ones on Kather classification, multiple instance learning, and 5 dense-prediction tasks.
  arXiv  Detail & Related papers  (2023-09-14T02:31:18Z)
• Rapid Training Data Creation by Synthesizing Medical Images for Classification and Localization [10.506584969668792]
  We present a method for the transformation of real data to train any Deep Neural Network to solve the above problems. For the weakly supervised model, we show that the localization accuracy increases significantly using the generated data. In the latter model, we show that the accuracy, when trained with generated images, closely parallels the accuracy when trained with exhaustively annotated real images.
  arXiv  Detail & Related papers  (2023-08-09T03:49:12Z)
• Optimizations of Autoencoders for Analysis and Classification of Microscopic In Situ Hybridization Images [68.8204255655161]
  We propose a deep-learning framework to detect and classify areas of microscopic images with similar levels of gene expression. The data we analyze requires an unsupervised learning model for which we employ a type of Artificial Neural Network - Deep Learning Autoencoders.
  arXiv  Detail & Related papers  (2023-04-19T13:45:28Z)
• Realistic Data Enrichment for Robust Image Segmentation in Histopathology [2.248423960136122]
  We propose a new approach, based on diffusion models, which can enrich an imbalanced dataset with plausible examples from underrepresented groups. Our method can simply expand limited clinical datasets making them suitable to train machine learning pipelines.
  arXiv  Detail & Related papers  (2023-04-19T09:52:50Z)
• Vision-Language Modelling For Radiological Imaging and Reports In The Low Data Regime [70.04389979779195]
  This paper explores training medical vision-language models (VLMs) where the visual and language inputs are embedded into a common space. We explore several candidate methods to improve low-data performance, including adapting generic pre-trained models to novel image and text domains. Using text-to-image retrieval as a benchmark, we evaluate the performance of these methods with variable sized training datasets of paired chest X-rays and radiological reports.
  arXiv  Detail & Related papers  (2023-03-30T18:20:00Z)
• Learning Representations with Contrastive Self-Supervised Learning for Histopathology Applications [8.69535649683089]
  We show how contrastive self-supervised learning can reduce the annotation effort within digital pathology. Our results pave the way for realizing the full potential of self-supervised learning for histopathology applications.
  arXiv  Detail & Related papers  (2021-12-10T16:08:57Z)
• Unlabeled Data Guided Semi-supervised Histopathology Image Segmentation [34.45302976822067]
  Semi-supervised learning (SSL) based on generative methods has been proven to be effective in utilizing diverse image characteristics. We propose a new data guided generative method for histopathology image segmentation by leveraging the unlabeled data distributions. Our method is evaluated on glands and nuclei datasets.
  arXiv  Detail & Related papers  (2020-12-17T02:54:19Z)
• Towards an Automatic Analysis of CHO-K1 Suspension Growth in Microfluidic Single-cell Cultivation [63.94623495501023]
  We propose a novel Machine Learning architecture, which allows us to infuse a neural deep network with human-powered abstraction on the level of data. Specifically, we train a generative model simultaneously on natural and synthetic data, so that it learns a shared representation, from which a target variable, such as the cell count, can be reliably estimated.
  arXiv  Detail & Related papers  (2020-10-20T08:36:51Z)
• Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction [55.94378672172967]
  We focus on few-shot disease subtype prediction problem, identifying subgroups of similar patients. We introduce meta learning techniques to develop a new model, which can extract the common experience or knowledge from interrelated clinical tasks. Our new model is built upon a carefully designed meta-learner, called Prototypical Network, that is a simple yet effective meta learning machine for few-shot image classification.
  arXiv  Detail & Related papers  (2020-09-02T02:50:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.