Visual representation of how Cell-TRACTR uses track queries to track cells and detect divisions. حسب Owen M. O’Connor (11965460)

الموضوعات:

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

الموضوعات:

Cell-TRACTR detects cells in the first frame at time t 0. Object queries predicted to be a “cell” are converted to track queries. For Track Group Denoising (TGD), random noise is a... حسب Owen M. O’Connor (11965460)

الموضوعات:

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... حسب Owen M. O’Connor (11965460)

الموضوعات:

<b>Resting ECG Segmentation Dataset</b> حسب Yuhang Liu (21210833)

"…<br><br>Rhythm Type Records num</li><li>AF (Atrial Flutter) 400</li><li>AFIB (Atrial Fibrillation) 400</li><li>AT (Atrial Tachycardia) 121</li><li>SB (Sinus Bradycardia) 400</li><li>SI (Sinus Irregularity) 399</li><li>SR (Sinus Rhythm) 400</li><li>ST (Sinus Tachycardia) 140</li><li>SVT (Supraventricular Tachycardia) 139</li><li>Total 2399</li></ul><p><br></p><p dir="ltr">The combination of high-quality beat-level labels and broad rhythm coverage makes RDB a strong benchmark for developing and evaluating ECG segmentation algorithms that must generalise across diverse clinical presentations.…"

Data_Sheet_1_Corneal stromal mapping characteristics in normal corneas using anterior segment SD-OCT.docx حسب Mohammad Abusamak (10712848)

Symbols and abbreviations in this paper. حسب Jian Lin (323268)

"…<div><p>In unsupervised transfer learning for medical image segmentation, where existing algorithms face the challenge of error propagation due to inaccessible source domain data. …"

Sensitivity analysis of parameters M and K. حسب Jian Lin (323268)

"…<div><p>In unsupervised transfer learning for medical image segmentation, where existing algorithms face the challenge of error propagation due to inaccessible source domain data. …"

Information on the datasets used in this study. حسب Jian Lin (323268)

"…<div><p>In unsupervised transfer learning for medical image segmentation, where existing algorithms face the challenge of error propagation due to inaccessible source domain data. …"

The image shows the predicted boundary boxes (in red) generated along with their respective segmented images (in random color) after passing each volume slice through the Mask R-CN... حسب Yubraj Gupta (7465394)

الموضوعات:

Proposed pipeline for the training and testing using the four datasets. حسب Yubraj Gupta (7465394)

الموضوعات:

The figure displays the final segmentation results achieved by applying the ensemble learning weight average fusion technique across four datasets, presented alongside their respec... حسب Yubraj Gupta (7465394)

الموضوعات:

The image illustrates the predicted output of Mask R-CNN (green channel) overlapped with the ground-truth image (red channel) for various datasets. حسب Yubraj Gupta (7465394)

الموضوعات:

Post-processing stage. حسب Yubraj Gupta (7465394)

الموضوعات:

Fig 13 - حسب Yubraj Gupta (7465394)

الموضوعات:

The image displays the comparison between the predicted output of the 3D U-Net model (shown in the green channel) and the ground-truth image (shown in the red channel) for various... حسب Yubraj Gupta (7465394)

الموضوعات:

Pre-processing steps. حسب Yubraj Gupta (7465394)

الموضوعات:

Four publicly available datasets, (a) Lucchi, (b) Lucchi++, (c) Kasthuri++, and (d) UroCell were used in this study. حسب Yubraj Gupta (7465394)

الموضوعات:

The image shows the predicted mask generated for all four datasets after passing each volume through a 3D U-Net trained model (M2). حسب Yubraj Gupta (7465394)

الموضوعات:

Summary of model configurations and computational complexities of each model. حسب Yubraj Gupta (7465394)

الموضوعات: