Multi_Scale_Tools: A Python Library to Exploit Multi-Scale Whole Slide Images

Marini, Niccolò; Otálora, Sebastian; Podareanu, Damian; Rijthoven, Mart van; Laak, Jeroen van der; Ciompi, Francesco; Müller, Henning; Atzori, Manfredo

doi:10.3389/fcomp.2021.684521

Cited by 20 publications

(12 citation statements)

References 40 publications

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“…In this paper, multi-magnification images refer to images with different resolutions in the same field of view (FOV), while multi-scale images have different FOVs at the same or different resolutions. The method of multi-scale features [43] is thus different from those of the two we mentioned earlier. An example of each type of image pair is displayed in Fig.…”

Section: A Multi-magnification Histopathological Imagementioning

confidence: 90%

Deep Multi-Magnification Similarity Learning for Histopathological Image Classification

Diao

Luo

Hou

et al. 2023

IEEE J. Biomed. Health Inform.

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Precise classification of histopathological images is crucial to computer-aided diagnosis in clinical practice. Magnification-based learning networks have attracted considerable attention for their ability to improve performance in histopathological classification. However, the fusion of pyramids of histopathological images at different magnifications is an under-explored area. In this paper, we proposed a novel deep multi-magnification similarity learning (DSML) approach that can be useful for the interpretation of multi-magnification learning framework and easy to visualize feature representation from low-dimension (e.g., cell-level) to high-dimension (e.g., tissue-level), which has overcome the difficulty of understanding cross-magnification information propagation. It uses a similarity cross entropy loss function designation to simultaneously learn the similarity of the information among cross-magnifications. In order to verify the effectiveness of DMSL, experiments with different network backbones and different magnification combinations were designed, and its ability to interpret was also investigated through visualization. Our experiments were performed on two different histopathological datasets: a clinical nasopharyngeal carcinoma and a public breast cancer BCSS2021 dataset.The results show that our method achieved outstanding performance in classification with a higher value of area under curve, accuracy, and F-score than other comparable methods. Moreover, the reasons behind multimagnification effectiveness were discussed.

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Section: A Multi-magnification Histopathological Imagementioning

confidence: 90%

Deep Multi-Magnification Similarity Learning for Histopathological Image Classification

Diao

Luo

Hou

et al. 2023

IEEE J. Biomed. Health Inform.

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“…WSIs splitting is necessary to the gigapixel nature of WSIs, since modern GPUs hardware has limited memory and cannot handle large images. Images are split into patches of 224 × 224 pixels, extracted from magnification 10x, using Multi_Scale_Tools library 47 . The patch size is chosen considering that the ResNet34 backbone used as CNN requires this input data size.…”

Section: Methodsmentioning

confidence: 99%

Unleashing the potential of digital pathology data by training computer-aided diagnosis models without human annotations

et al. 2022

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The digitalization of clinical workflows and the increasing performance of deep learning algorithms are paving the way towards new methods for tackling cancer diagnosis. However, the availability of medical specialists to annotate digitized images and free-text diagnostic reports does not scale with the need for large datasets required to train robust computer-aided diagnosis methods that can target the high variability of clinical cases and data produced. This work proposes and evaluates an approach to eliminate the need for manual annotations to train computer-aided diagnosis tools in digital pathology. The approach includes two components, to automatically extract semantically meaningful concepts from diagnostic reports and use them as weak labels to train convolutional neural networks (CNNs) for histopathology diagnosis. The approach is trained (through 10-fold cross-validation) on 3’769 clinical images and reports, provided by two hospitals and tested on over 11’000 images from private and publicly available datasets. The CNN, trained with automatically generated labels, is compared with the same architecture trained with manual labels. Results show that combining text analysis and end-to-end deep neural networks allows building computer-aided diagnosis tools that reach solid performance (micro-accuracy = 0.908 at image-level) based only on existing clinical data without the need for manual annotations.

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“…Both WSIs and ROIs are split in patches of dimension 224x224 from magnification 10x, as shown in [11]. WSIs are split in a grid and only patches including tissue are extracted [12].…”

Section: Data Structure and Pre-processingmentioning

confidence: 99%

A Multi-Task Multiple Instance Learning Algorithm to Analyze Large Whole Slide Images from Bright Challenge 2022

Marini

Wodziński

Atzori

et al. 2022

2022 IEEE International Symposium on Biomedical Imaging Challenges (ISBIC)

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Malignant lesions in breast tissue specimen whole slide images (WSIs), may lead to a dangerous diagnosis, such as cancer. However, WSIs analysis is time-consuming and expensive, requiring the work of expert pathologists. This paper aims to present a method for the 2022 BRIGHT Challenge, that involves the analysis of breast WSIs. The organizers provided over 550 breast WSIs and over 3900 regions of interest (ROIs) to develop and validate methods for breast cancer images. The method presented in this work is based on a Multiple Instance Learning instance-based Convolutional Neural Network (CNN), allowing the combination of strongly-annotated data (from ROIs) and weakly-annotated data (from WSIs) via the optimization of a multi-task loss function. Furthermore, during the CNN training, the input patches are clustered and filtered according to their entropy, to reduce the non-informative content used to train the model. The CNN reaches an averaged F1-score = 0.63 ± 0.02 on the 3-class classification task and averaged F1-score = 0.39 ± 0.08 on the 6-class classification task, considering the validation partition; an averaged F1-score = 0.65 on the cancer risk classification task and averaged F1-score = 0.45 on the sub-typing cancer risk classification task, considering the best result achieved on the test partition. These results show that Multiple Instance Learning instance-based CNNs may represent a good resource to tackle this kind of problem.

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Multi_Scale_Tools: A Python Library to Exploit Multi-Scale Whole Slide Images

Cited by 20 publications

References 40 publications

Deep Multi-Magnification Similarity Learning for Histopathological Image Classification

Deep Multi-Magnification Similarity Learning for Histopathological Image Classification

Unleashing the potential of digital pathology data by training computer-aided diagnosis models without human annotations

A Multi-Task Multiple Instance Learning Algorithm to Analyze Large Whole Slide Images from Bright Challenge 2022

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