Taxonomy and evolution predicting using deep learning in images
- URL: http://arxiv.org/abs/2206.14011v1
- Date: Tue, 28 Jun 2022 13:54:14 GMT
- Title: Taxonomy and evolution predicting using deep learning in images
- Authors: Jiewen Xiao, Wenbin Liao, Ming Zhang, Jing Wang, Jianxin Wang, Yihua
Yang
- Abstract summary: This study creates a novel recognition framework by systematically studying the mushroom image recognition problem.
We present the first method to map images to DNA, namely used an encoder mapping image to genetic distances, and then decoded DNA through a pre-trained decoder.
- Score: 9.98733710208427
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Molecular and morphological characters, as important parts of biological
taxonomy, are contradictory but need to be integrated. Organism's image
recognition and bioinformatics are emerging and hot problems nowadays but with
a gap between them. In this work, a multi-branching recognition framework
mediated by genetic information bridges this barrier, which establishes the
link between macro-morphology and micro-molecular information of mushrooms. The
novel multi-perspective structure is proposed to fuse the feature images from
three branching models, which significantly improves the accuracy of
recognition by about 10% and up to more than 90%. Further, genetic information
is implemented to the mushroom image recognition task by using genetic distance
embeddings as the representation space for predicting image distance and
species identification. Semantic overfitting of traditional classification
tasks and the granularity of fine-grained image recognition are also discussed
in depth for the first time. The generalizability of the model was investigated
in fine-grained scenarios using zero-shot learning tasks, which could predict
the taxonomic and evolutionary information of unseen samples. We presented the
first method to map images to DNA, namely used an encoder mapping image to
genetic distances, and then decoded DNA through a pre-trained decoder, where
the total test accuracy on 37 species for DNA prediction is 87.45%. This study
creates a novel recognition framework by systematically studying the mushroom
image recognition problem, bridging the gap between macroscopic biological
information and microscopic molecular information, which will provide a new
reference for intelligent biometrics in the future.
Related papers
- CLIBD: Bridging Vision and Genomics for Biodiversity Monitoring at Scale [21.995678534789615]
We use contrastive learning to align images, barcode DNA, and text-based representations of taxonomic labels in a unified embedding space.
Our method surpasses previous single-modality approaches in accuracy by over 8% on zero-shot learning tasks.
arXiv Detail & Related papers (2024-05-27T17:57:48Z) - Learning Multimodal Volumetric Features for Large-Scale Neuron Tracing [72.45257414889478]
We aim to reduce human workload by predicting connectivity between over-segmented neuron pieces.
We first construct a dataset, named FlyTracing, that contains millions of pairwise connections of segments expanding the whole fly brain.
We propose a novel connectivity-aware contrastive learning method to generate dense volumetric EM image embedding.
arXiv Detail & Related papers (2024-01-05T19:45:12Z) - Genetic InfoMax: Exploring Mutual Information Maximization in
High-Dimensional Imaging Genetics Studies [50.11449968854487]
Genome-wide association studies (GWAS) are used to identify relationships between genetic variations and specific traits.
Representation learning for imaging genetics is largely under-explored due to the unique challenges posed by GWAS.
We introduce a trans-modal learning framework Genetic InfoMax (GIM) to address the specific challenges of GWAS.
arXiv Detail & Related papers (2023-09-26T03:59:21Z) - Discovering Novel Biological Traits From Images Using Phylogeny-Guided
Neural Networks [10.372001949268636]
We present a novel approach for discovering evolutionary traits directly from images without relying on trait labels.
Our proposed approach, Phylo-NN, encodes the image of an organism into a sequence of quantized feature vectors.
We demonstrate the effectiveness of our approach in producing biologically meaningful results in a number of downstream tasks.
arXiv Detail & Related papers (2023-06-05T20:22:05Z) - Unsupervised ensemble-based phenotyping helps enhance the
discoverability of genes related to heart morphology [57.25098075813054]
We propose a new framework for gene discovery entitled Un Phenotype Ensembles.
It builds a redundant yet highly expressive representation by pooling a set of phenotypes learned in an unsupervised manner.
These phenotypes are then analyzed via (GWAS), retaining only highly confident and stable associations.
arXiv Detail & Related papers (2023-01-07T18:36:44Z) - Mushroom image recognition and distance generation based on
attention-mechanism model and genetic information [4.845860279763184]
We propose a new model based on attention-mechanism, MushroomNet, which applies the lightweight network MobileNetV3 as the backbone model.
On the public dataset, the test accuracy of the MushroomNet model has reached 83.9%, and on the local dataset, the test accuracy has reached 77.4%.
We found that using the MES activation function can predict the genetic distance of mushrooms very well, but the accuracy is lower than that of SoftMax.
arXiv Detail & Related papers (2022-06-27T15:43:03Z) - Learning multi-scale functional representations of proteins from
single-cell microscopy data [77.34726150561087]
We show that simple convolutional networks trained on localization classification can learn protein representations that encapsulate diverse functional information.
We also propose a robust evaluation strategy to assess quality of protein representations across different scales of biological function.
arXiv Detail & Related papers (2022-05-24T00:00:07Z) - Self-Supervised Vision Transformers Learn Visual Concepts in
Histopathology [5.164102666113966]
We conduct a search for good representations in pathology by training a variety of self-supervised models with validation on a variety of weakly-supervised and patch-level tasks.
Our key finding is in discovering that Vision Transformers using DINO-based knowledge distillation are able to learn data-efficient and interpretable features in histology images.
arXiv Detail & Related papers (2022-03-01T16:14:41Z) - Evolution Is All You Need: Phylogenetic Augmentation for Contrastive
Learning [1.7188280334580197]
Self-supervised representation learning of biological sequence embeddings alleviates computational resource constraints on downstream tasks.
We show that contrastive learning using evolutionary phylogenetic augmentation can be used as a representation learning objective.
arXiv Detail & Related papers (2020-12-25T01:35:06Z) - Deep Low-Shot Learning for Biological Image Classification and
Visualization from Limited Training Samples [52.549928980694695]
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.
arXiv Detail & Related papers (2020-10-20T06:06:06Z) - Automatic image-based identification and biomass estimation of
invertebrates [70.08255822611812]
Time-consuming sorting and identification of taxa pose strong limitations on how many insect samples can be processed.
We propose to replace the standard manual approach of human expert-based sorting and identification with an automatic image-based technology.
We use state-of-the-art Resnet-50 and InceptionV3 CNNs for the classification task.
arXiv Detail & Related papers (2020-02-05T21:38:57Z)
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.