Paper Citation Record · LEDGER

Enhancing AI microscopy for foodborne bacterial classification via adversarial domain adaptation across optical and biological variability

As of 12 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2411.19514.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2411.19514 v1

Coverage vector

measured 30 of 30 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

30 of 30 outbound references displayed

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External citation measurements

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Outbound references

Observation edeb1cb6-329b-45d0-b6f7-67aabb6e16a6 · outbound

This paper cites Albumentations: fast and flexible image augmentations.

Enhancing AI microscopy for foodborne bacterial classification via adversarial domain adaptation across optical and biological variability Albumentations: fast and flexible image augmentations

Reference 1

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This paper cites Progressive feature alignment for unsupervised domain adaptation, in: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp.

Enhancing AI microscopy for foodborne bacterial classification via adversarial domain adaptation across optical and biological variability Progressive feature alignment for unsupervised domain adaptation, in: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp

Reference 2

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Observation 4000e8a6-cf0c-4d5d-8293-3a49ed95469b · outbound

This paper cites Microscopic identification of foodborne bacterial pathogens based on deep learning method.

Enhancing AI microscopy for foodborne bacterial classification via adversarial domain adaptation across optical and biological variability Microscopic identification of foodborne bacterial pathogens based on deep learning method

Reference 3

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This paper cites Bacterial image analysis using multi-task deep learning approaches for clinical microscopy.

Enhancing AI microscopy for foodborne bacterial classification via adversarial domain adaptation across optical and biological variability Bacterial image analysis using multi-task deep learning approaches for clinical microscopy

Reference 4

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Observation 581e4a0c-5112-45fd-a2a5-2c82f963d0a0 · outbound

This paper cites Microbial detection and identification methods: Bench top assays to omics approaches.

Enhancing AI microscopy for foodborne bacterial classification via adversarial domain adaptation across optical and biological variability Microbial detection and identification methods: Bench top assays to omics approaches

Reference 5

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Observation 28fbf509-b594-48ea-afaa-c9e9ef3f2f00 · outbound

This paper cites Domain-adversarial training of neural networks.

Enhancing AI microscopy for foodborne bacterial classification via adversarial domain adaptation across optical and biological variability Domain-adversarial training of neural networks

Reference 6

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This paper cites Generalizing microscopy image labeling via layer-matching adversarial domain adaptation, in: ICML’24 Workshop ML for Life and Material Science: From Theory to Industry Applications.

Enhancing AI microscopy for foodborne bacterial classification via adversarial domain adaptation across optical and biological variability Generalizing microscopy image labeling via layer-matching adversarial domain adaptation, in: ICML’24 Workshop ML for Life and Material Science: From Theory to Industry Applications

Reference 7

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This paper cites Stochastic neighbor embedding, in: Becker, S., Thrun, S., Obermayer, K.

Enhancing AI microscopy for foodborne bacterial classification via adversarial domain adaptation across optical and biological variability Stochastic neighbor embedding, in: Becker, S., Thrun, S., Obermayer, K

Reference 8

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Observation dea9a3e9-eac8-4d28-bef4-fd58bcf1aed0 · outbound

This paper cites Economic burden of foodborne illnesses acquired in the united states.

Enhancing AI microscopy for foodborne bacterial classification via adversarial domain adaptation across optical and biological variability Economic burden of foodborne illnesses acquired in the united states

Reference 9

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Observation 746f88b9-8494-42a7-821e-b7c97e232124 · outbound

This paper cites Advances and opportunities in image analysis of bacterial cells and communities.

Enhancing AI microscopy for foodborne bacterial classification via adversarial domain adaptation across optical and biological variability Advances and opportunities in image analysis of bacterial cells and communities

Reference 10

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Observation 273365a2-c190-486f-becc-b0ed8c317166 · outbound

This paper cites Accelerating the detection of bacteria in food using artificial intelligence and optical imaging.

Enhancing AI microscopy for foodborne bacterial classification via adversarial domain adaptation across optical and biological variability Accelerating the detection of bacteria in food using artificial intelligence and optical imaging

Reference 11

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This paper cites Deep learning-based image processing in optical microscopy.

Enhancing AI microscopy for foodborne bacterial classification via adversarial domain adaptation across optical and biological variability Deep learning-based image processing in optical microscopy

Reference 12

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Observation 21680c9a-4954-4add-b57f-792431e0bf9a · outbound

This paper cites Domain adapted multitask learning for segmenting amoeboid cells in microscopy.

Enhancing AI microscopy for foodborne bacterial classification via adversarial domain adaptation across optical and biological variability Domain adapted multitask learning for segmenting amoeboid cells in microscopy

Reference 13

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This paper cites End-to-end prediction of uniaxial compression profiles of apples during in vitro digestion using time-series micro-computed tomography and deep learning.

Enhancing AI microscopy for foodborne bacterial classification via adversarial domain adaptation across optical and biological variability End-to-end prediction of uniaxial compression profiles of apples during in vitro digestion using time-series micro-computed tomography and deep learning

Reference 14

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Observation ed607861-a373-4285-9aa6-8c5ec57398c0 · outbound

This paper cites Segmentation of cell-level anomalies in electroluminescence images of photovoltaic modules.

Enhancing AI microscopy for foodborne bacterial classification via adversarial domain adaptation across optical and biological variability Segmentation of cell-level anomalies in electroluminescence images of photovoltaic modules

Reference 15

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Observation d067dc94-820e-4e9e-a0b1-5ba9426e5c83 · outbound

This paper cites Multi-adversarial domain adaptation.

Enhancing AI microscopy for foodborne bacterial classification via adversarial domain adaptation across optical and biological variability Multi-adversarial domain adaptation

Reference 16

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Observation 22ebf3c6-4e7b-4698-a386-eb961cd58495 · outbound

This paper cites Food recalls associated with foodborne disease outbreaks, united states, 2006–2016.

Enhancing AI microscopy for foodborne bacterial classification via adversarial domain adaptation across optical and biological variability Food recalls associated with foodborne disease outbreaks, united states, 2006–2016

Reference 17

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This paper cites Biointel: Real-time bacteria identification using microscopy imaging, in: 2024 IEEE International Symposium on Biomedical Imaging (ISBI), pp.

Enhancing AI microscopy for foodborne bacterial classification via adversarial domain adaptation across optical and biological variability Biointel: Real-time bacteria identification using microscopy imaging, in: 2024 IEEE International Symposium on Biomedical Imaging (ISBI), pp

Reference 18

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Enhancing AI microscopy for foodborne bacterial classification via adversarial domain adaptation across optical and biological variability NIH image to ImageJ: 25 years of image analysis

Reference 19

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This paper cites Grad-CAM: Visual explanations from deep networks via gradient-based localization, in: Proceedings of the IEEE International Conference on Computer Vision, pp.

Enhancing AI microscopy for foodborne bacterial classification via adversarial domain adaptation across optical and biological variability Grad-CAM: Visual explanations from deep networks via gradient-based localization, in: Proceedings of the IEEE International Conference on Computer Vision, pp

Reference 20

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This paper cites Twist formulas for one-row colored $A_2$ webs and $\mathfrak{sl}_3$ tails of $(2,2m)$-torus links.

Enhancing AI microscopy for foodborne bacterial classification via adversarial domain adaptation across optical and biological variability Twist formulas for one-row colored $A_2$ webs and $\mathfrak{sl}_3$ tails of $(2,2m)$-torus links

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This paper cites Efficientnet: Rethinking model scaling for convolutional neural networks, in: International Conference on Machine Learning, pp.

Enhancing AI microscopy for foodborne bacterial classification via adversarial domain adaptation across optical and biological variability Efficientnet: Rethinking model scaling for convolutional neural networks, in: International Conference on Machine Learning, pp

Reference 22

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This paper cites Efficientnetv2: Smaller models and faster training, in: International Conference on Machine Learning, pp.

Enhancing AI microscopy for foodborne bacterial classification via adversarial domain adaptation across optical and biological variability Efficientnetv2: Smaller models and faster training, in: International Conference on Machine Learning, pp

Reference 23

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This paper cites Multi-task multi- domain learning for digital staining and classification of leukocytes.

Enhancing AI microscopy for foodborne bacterial classification via adversarial domain adaptation across optical and biological variability Multi-task multi- domain learning for digital staining and classification of leukocytes

Reference 24

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This paper cites Rapid counting of coliforms andEscherichia coli by deep learning-based classifier.

Enhancing AI microscopy for foodborne bacterial classification via adversarial domain adaptation across optical and biological variability Rapid counting of coliforms andEscherichia coli by deep learning-based classifier

Reference 25

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Observation a52949c8-be3f-4c83-bf2a-7f19a266eebc · outbound

This paper cites Semi-supervised cell instance segmentation for multi-modality microscope images, in: Ma, J., Xie, R., Gupta, A., Guilherme de Almeida, J., Bader, G.D., Wang, B.

Enhancing AI microscopy for foodborne bacterial classification via adversarial domain adaptation across optical and biological variability Semi-supervised cell instance segmentation for multi-modality microscope images, in: Ma, J., Xie, R., Gupta, A., Guilherme de Almeida, J., Bader, G.D., Wang, B

Reference 26

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This paper cites Machine learning algorithms in microbial classification: a comparative analysis.

Enhancing AI microscopy for foodborne bacterial classification via adversarial domain adaptation across optical and biological variability Machine learning algorithms in microbial classification: a comparative analysis

Reference 27

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Enhancing AI microscopy for foodborne bacterial classification via adversarial domain adaptation across optical and biological variability Bidirectional mapping-based domain adaptation for nucleus detection in cross-modality microscopy images

Reference 28

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This paper cites Te-yolof: Tiny and efficient yolof for blood cell detection.

Enhancing AI microscopy for foodborne bacterial classification via adversarial domain adaptation across optical and biological variability Te-yolof: Tiny and efficient yolof for blood cell detection

Reference 29

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Observation c7ae90c1-5f64-4726-a962-c4e2e76c1875 · outbound

This paper cites Adversarial multiple source domain adaptation, in: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R.

Enhancing AI microscopy for foodborne bacterial classification via adversarial domain adaptation across optical and biological variability Adversarial multiple source domain adaptation, in: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R

Reference 30

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Pith citing papers

No inbound Pith citation observations are available.