Detection of Necrosis in Digitised Whole-Slide Images for Better Grading of Canine Soft-Tissue Sarcomas Using Machine-Learning

Morisi, Ambra; Rai, Taran; Bacon, Nicholas J.; Thomas, Spencer A.; Bober, Miroslaw; Wells, Kevin; Dark, Michael J.; Aboellail, Tawfik; Bacci, Barbara; Ragione, Roberto M. La

doi:10.3390/vetsci10010045

Cited by 4 publications

(2 citation statements)

References 30 publications

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“…These scores ranged from 0 to 1, where 1 would highlight the model is 100% certain that the candidate is mitosis and 0.01 would describe a prediction that is very low in confidence. We optimised our models based on the F1-score [44][45][46]. The probability thresholds t ranged from 0.01 to 1, and so choosing the optimal threshold T for the F1-score F1 can be represented formally as:…”

Section: Adaptive F1-score Thresholdmentioning

confidence: 99%

Keeping Pathologists in the Loop and an Adaptive F1-Score Threshold Method for Mitosis Detection in Canine Perivascular Wall Tumours

Rai,

Morisi,

Bacci

et al. 2024

Cancers

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Performing a mitosis count (MC) is the diagnostic task of histologically grading canine Soft Tissue Sarcoma (cSTS). However, mitosis count is subject to inter- and intra-observer variability. Deep learning models can offer a standardisation in the process of MC used to histologically grade canine Soft Tissue Sarcomas. Subsequently, the focus of this study was mitosis detection in canine Perivascular Wall Tumours (cPWTs). Generating mitosis annotations is a long and arduous process open to inter-observer variability. Therefore, by keeping pathologists in the loop, a two-step annotation process was performed where a pre-trained Faster R-CNN model was trained on initial annotations provided by veterinary pathologists. The pathologists reviewed the output false positive mitosis candidates and determined whether these were overlooked candidates, thus updating the dataset. Faster R-CNN was then trained on this updated dataset. An optimal decision threshold was applied to maximise the F1-score predetermined using the validation set and produced our best F1-score of 0.75, which is competitive with the state of the art in the canine mitosis domain.

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Section: Adaptive F1-score Thresholdmentioning

confidence: 99%

Keeping Pathologists in the Loop and an Adaptive F1-Score Threshold Method for Mitosis Detection in Canine Perivascular Wall Tumours

Rai,

Morisi,

Bacci

et al. 2024

Cancers

Self Cite

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“…In this paper we use necrosis detection 13,14,15 as an exemplar problem in computational pathology drawn from a canine Perivascular Wall Tumours (cPWT) data set. An external veterinary pathologist diagnosed Canine Soft Tissue Sarcoma (cSTS) histology slides obtained from the Department of Microbiology, Immunology and Pathology, Colorado State University.…”

Section: Task Data and Patch Extraction Processmentioning

confidence: 99%

Investigating the potential of untrained convolutional layers and pruning in computational pathology

Rai

Papanikolaou

Dave

et al. 2023

Medical Imaging 2023: Digital and Computational Pathology

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The effectiveness of untrained convolutional layers for feature extraction in a computational pathology task using real-world data from a necrosis detection dataset is investigated. The study aims to determine whether ImageNet pretrained layers from deep CNNs combined with frozen untrained weights are sufficient for effective necrosis detection in canine Perivascular Wall Tumour (cPWT) whole slide images. Additionally, the authors investigate the impact of pruning CNNs, and whether it can be effective for necrosis detection as this technique can contribute towards reducing memory requirements and improve inference speed in diagnostic settings. The study found that fine-tuning the last (deepest) layers of a pretrained ImageNet model for necrosis detection in cPWT produces the highest test F1-score (0.715) when compared to alternative set ups. This score is further improved to 0.754 when the results are optimised using an optimal threshold predetermined on maximising the validation set F1-score. Resetting weights (untrained) and freezing the last few convolutional layers in the last dense block also demonstrated some capability in necrosis detection with an optimised F1-score of 0.747, still outperforming models trained from scratch as well as an ImageNet pretrained feature extraction model. Pruning the fine-tuned model using lower thresholds also showed the potential to improve performance, however thresholds higher than 0.40 negatively impacted performance.

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Evaluating diffusion model generated synthetic histopathology image data against authentic digital pathology images

Rai,

Gola,

Hernández

et al. 2024

Medical Imaging 2024: Digital and Computational Pathology

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This study explores the efficacy of diffusion probabilistic models for generating synthetic histopathological images, specifically canine Perivascular Wall Tumours (cPWT), to supplement limited datasets for deep learning applications in digital pathology. This research evaluates an open-source medical domain-focused diffusion model called Medfusion, where the model was trained on a small (1,000 patches) and a large dataset (17,000 patches) of cPWT images to compare performance on the different sized datasets. A Receiver Operating Characteristic (ROC) study was implemented to investigate the ability of six veterinary medical professionals and pathologists to discern between generated and real cPWT patch images. The participants engaged in two separate rounds, where each round corresponded to models that had been trained on the two different sized datasets. The ROC study revealed mean average Area Under the Curve (AUC) values close to 0.5 for both rounds. The results from this study suggests that diffusion models can create histopathological patch images that are convincingly realistic where our participants often struggled to reliably differentiate between generated and real images. This underscores the potential of these models as a valuable tool for augmenting digital pathology datasets.

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Detection of Necrosis in Digitised Whole-Slide Images for Better Grading of Canine Soft-Tissue Sarcomas Using Machine-Learning

Cited by 4 publications

References 30 publications

Keeping Pathologists in the Loop and an Adaptive F1-Score Threshold Method for Mitosis Detection in Canine Perivascular Wall Tumours

Keeping Pathologists in the Loop and an Adaptive F1-Score Threshold Method for Mitosis Detection in Canine Perivascular Wall Tumours

Investigating the potential of untrained convolutional layers and pruning in computational pathology

Evaluating diffusion model generated synthetic histopathology image data against authentic digital pathology images

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