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Showing 201 - 220 results of 1,315 for search '(((( data code algorithm ) OR ( data tracking algorithm ))) OR ( element network algorithm ))', query time: 0.43s Refine Results
  1. 201
    Time-lapse microscopy movie from the bacterial mother machine test set processed by Cell-TRACTR.

    Time-lapse microscopy movie from the bacterial mother machine test set processed by Cell-TRACTR. by Owen M. O’Connor (11965460)

    Published 2025
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  2. 202
    Example illustrating how object and track queries interact using an attention map processed by Cell-TRACTR across two subsequent frames. The schematic displays the actual output alongside the output the from top matching object queries. Extracted from the self-attention mechanism in the decoder, the attention map highlights the strength of interactions among the queries. Lighter colors indicate stronger interactions. The track queries tend to focus on themselves, while the object queries attend to the track query that corresponds most closely in location.

    Example illustrating how object and track queries interact using an attention map processed by Cell-TRACTR across two subsequent frames. The schematic displays the actual output al... by Owen M. O’Connor (11965460)

    Published 2025
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  3. 203
    (A) Example illustrating the correlation between the final prediction of an object query and where the object query is derived from within the multi-scale features. Colored pixels represent multi-scale features that were classified as “cell” by the decoder. Black pixels represent multi-scale feature pixels that were classified as “no object” by the decoder. The colored pixels are overlaid onto the phase contrast image for visualization purposes. (B) A more complex example illustrating the correlation between the multi-scale features and object queries.

    (A) Example illustrating the correlation between the final prediction of an object query and where the object query is derived from within the multi-scale features. Colored pixels... by Owen M. O’Connor (11965460)

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  4. 204
    Heatmap illustrating which multi-scale feature pixels were classified as “cell” by query selection. This data was collected by processing the full test set for the DeepCell dataset. Note that for these movies, Cell-TRACTR preferentially uses pixels from the highest resolution multi-scale features.

    Heatmap illustrating which multi-scale feature pixels were classified as “cell” by query selection. This data was collected by processing the full test set for the DeepCell dataset... by Owen M. O’Connor (11965460)

    Published 2025
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  5. 205
    The encoder employs deformable self-attention to transform multi-scale features into encoded multi-scale features. Query selection is used to derive the object queries from these encoded multi-scale features. Each encoded image feature undergoes classification, and the top-K features are selected as the content embeddings for the object queries. Additionally, bounding boxes are extracted from the top-K segmentation masks and are subsequently converted into positional embeddings. Following this, the object queries perform self-attention and deformable cross-attention with the encoded multi-scale features to produce the output embeddings. MLP, multi-layer perceptron.

    The encoder employs deformable self-attention to transform multi-scale features into encoded multi-scale features. Query selection is used to derive the object queries from these e... by Owen M. O’Connor (11965460)

    Published 2025
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  6. 206
    Schematic demonstrating how Cell-TRACTR tracks cells post-division. The dark blue cell at time t<sub>1</sub> is predicted to divide at time t<sub>2</sub>. The content embedding is duplicated for both divided cells. The positional embeddings are generated from each divided cell’s segmentation mask. The content embeddings are shown as squares and the positional embeddings are circles.

    Schematic demonstrating how Cell-TRACTR tracks cells post-division. The dark blue cell at time t<sub>1</sub> is predicted to divide at time t<sub>2</sub>. The content embedding is... by Owen M. O’Connor (11965460)

    Published 2025
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  7. 207
    Heatmap illustrating which multi-scale feature pixels were classified as “cell” by query selection. This data was collected by processing the full test set containing 29 movies from the bacterial mother machine dataset with Cell-TRACTR. Note that for these movies, the model preferentially uses pixels from the lowest resolution multi-scale features. The black pixel at the top corresponds to the top of the mother machine image where there are not usually cells.

    Heatmap illustrating which multi-scale feature pixels were classified as “cell” by query selection. This data was collected by processing the full test set containing 29 movies fro... by Owen M. O’Connor (11965460)

    Published 2025
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  8. 208
    Depiction of how predictions are made from the output embeddings. The largest encoded multi-scale feature map is resized and combined with the original multi-scale features to form the pixel embedding map. The dot product of the output embeddings and the pixel embedding map produces the segmentation masks. A linear layer is used to predict the class label and a multi-layer perceptron (MLP) is used to generate the bounding box. For output embeddings classified as “no object”, the bounding boxes and segmentation masks are ignored.

    Depiction of how predictions are made from the output embeddings. The largest encoded multi-scale feature map is resized and combined with the original multi-scale features to form... by Owen M. O’Connor (11965460)

    Published 2025
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  9. 209
    Effect of segmentation mask resolution on CUDA memory usage and performance metrics. (A) Memory usage as a function of queries when Cell-TRACTR produces a segmentation mask with the same resolution as the original image. (B) 1/4 Cell-TRACTR produces a segmentation mask with one-fourth the resolution of the input image.

    Effect of segmentation mask resolution on CUDA memory usage and performance metrics. (A) Memory usage as a function of queries when Cell-TRACTR produces a segmentation mask with th... by Owen M. O’Connor (11965460)

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  10. 210
    Diagram illustrating how the iterative bounding box refinement method works with cell divisions. When tracking the cell from t<sub>0</sub> to t<sub>1</sub>, the bounding box from t<sub>0</sub> serves as the initial bounding box that is iteratively refined three times into a bounding box that locates the cell in t<sub>1</sub>. The second bounding box is ignored here since cell division is not predicted at time t<sub>1</sub>. This method consists of predicting a relative offset with respect to the previous bounding box, represented by a chang

    Diagram illustrating how the iterative bounding box refinement method works with cell divisions. When tracking the cell from t<sub>0</sub> to t<sub>1</sub>, the bounding box from t... by Owen M. O’Connor (11965460)

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  11. 211
    Example showing how OP<sub>CTB</sub> can reward a tracker for predicting no division rather than predicting a division one frame too early or late. Acyclic oriented graph matching (AGOM) is the weighted sum of the operations needed to transform the tracker graph into the reference graph. A lower AGOM score is better and a higher TRA score is better.

    Example showing how OP<sub>CTB</sub> can reward a tracker for predicting no division rather than predicting a division one frame too early or late. Acyclic oriented graph matching... by Owen M. O’Connor (11965460)

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  12. 212
    Time-lapse microscopy movie from the mammalian DeepCell test set processed by Cell-TRACTR.

    Time-lapse microscopy movie from the mammalian DeepCell test set processed by Cell-TRACTR. by Owen M. O’Connor (11965460)

    Published 2025
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  13. 213
    Cell-HOTA metric incorporates cell division accuracy.

    Cell-HOTA metric incorporates cell division accuracy. by Owen M. O’Connor (11965460)

    Published 2025
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  14. 214
    Evaluation of model performance on the mammalian DeepCell dataset.

    Evaluation of model performance on the mammalian DeepCell dataset. by Owen M. O’Connor (11965460)

    Published 2025
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  15. 215
    Evaluation of model performance on the bacterial mother machine dataset.

    Evaluation of model performance on the bacterial mother machine dataset. by Owen M. O’Connor (11965460)

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  16. 216
    Time-lapse microscopy movie from the bacterial mother machine test set processed by Cell-TRACTR and evaluated with the Cell Tracking Challenge (CTC) OP<sub>CTB</sub> metrics.

    Time-lapse microscopy movie from the bacterial mother machine test set processed by Cell-TRACTR and evaluated with the Cell Tracking Challenge (CTC) OP<sub>CTB</sub> metrics. by Owen M. O’Connor (11965460)

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  17. 217
    Results from Cell-TRACTR and model overview for object detection.

    Results from Cell-TRACTR and model overview for object detection. by Owen M. O’Connor (11965460)

    Published 2025
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  18. 218
    Schematic demonstrating how Cell-HOTA handles early and late divisions. For early divisions, the ground truth cell is originally matched with one of the tracker cells at time t<sub>1</sub>. The red line indicates the matched cells, and the dotted blue box shows how the IOU is computed. To account for an early division, the IOU is computed between the ground truth cell in t<sub>1</sub> and both tracker cells in t<sub>1</sub>. In addition, the temporal association for the ground truth cell in t<sub>1</sub> is altered to match the tracker cell in t

    Schematic demonstrating how Cell-HOTA handles early and late divisions. For early divisions, the ground truth cell is originally matched with one of the tracker cells at time t<sub... by Owen M. O’Connor (11965460)

    Published 2025
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  19. 219
    Reflection matrix microscopy data and CLASS algorithms

    Reflection matrix microscopy data and CLASS algorithms by Sungsam Kang (18931031)

    Published 2025
    “…<h3><b>Reflection matrix microscopy data and CLASS algorithms</b></h3><p dir="ltr">This software package includes essential algorithms for reflection matrix microscope (RMM) and the CLASS algorithm. …”
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  20. 220
    Comparison between pyAKI’s generated labels and the labels generated by the MIMIC-code implementation of the KDIGO criteria.

    Comparison between pyAKI’s generated labels and the labels generated by the MIMIC-code implementation of the KDIGO criteria. by Christian Porschen (12967868)

    Published 2025
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