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Article ; Online: Annotating for Artificial Intelligence Applications in Digital Pathology: A Practical Guide for Pathologists and Researchers.

Montezuma, Diana / Oliveira, Sara P / Neto, Pedro C / Oliveira, Domingos / Monteiro, Ana / Cardoso, Jaime S / Macedo-Pinto, Isabel

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc

2023 Volume 36, Issue 4, Page(s) 100086

Abstract: Training machine learning models for artificial intelligence (AI) applications in pathology often requires extensive annotation by human experts, but there is little guidance on the subject. In this work, we aimed to describe our experience and provide a ...

Abstract	Training machine learning models for artificial intelligence (AI) applications in pathology often requires extensive annotation by human experts, but there is little guidance on the subject. In this work, we aimed to describe our experience and provide a simple, useful, and practical guide addressing annotation strategies for AI development in computational pathology. Annotation methodology will vary significantly depending on the specific study's objectives, but common difficulties will be present across different settings. We summarize key aspects and issue guiding principles regarding team interaction, ground-truth quality assessment, different annotation types, and available software and hardware options and address common difficulties while annotating. This guide was specifically designed for pathology annotation, intending to help pathologists, other researchers, and AI developers with this process.
MeSH term(s)	Humans ; Artificial Intelligence ; Pathologists ; Software ; Machine Learning
Language	English
Publishing date	2023-01-11
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	645073-8
ISSN	1530-0285 ; 0893-3952
ISSN (online)	1530-0285
ISSN	0893-3952
DOI	10.1016/j.modpat.2022.100086
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Author Correction: An interpretable machine learning system for colorectal cancer diagnosis from pathology slides.

Neto, Pedro C / Montezuma, Diana / Oliveira, Sara P / Oliveira, Domingos / Fraga, João / Monteiro, Ana / Monteiro, João / Ribeiro, Liliana / Gonçalves, Sofia / Reinhard, Stefan / Zlobec, Inti / Pinto, Isabel M / Cardoso, Jaime S

NPJ precision oncology

2024 Volume 8, Issue 1, Page(s) 83

Language	English
Publishing date	2024-04-03
Publishing country	England
Document type	Published Erratum
ISSN	2397-768X
ISSN	2397-768X
DOI	10.1038/s41698-024-00581-2
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: An interpretable machine learning system for colorectal cancer diagnosis from pathology slides.

NPJ precision oncology

2024 Volume 8, Issue 1, Page(s) 56

Abstract: Considering the profound transformation affecting pathology practice, we aimed to develop a scalable artificial intelligence (AI) system to diagnose colorectal cancer from whole-slide images (WSI). For this, we propose a deep learning (DL) system that ... ...

Abstract	Considering the profound transformation affecting pathology practice, we aimed to develop a scalable artificial intelligence (AI) system to diagnose colorectal cancer from whole-slide images (WSI). For this, we propose a deep learning (DL) system that learns from weak labels, a sampling strategy that reduces the number of training samples by a factor of six without compromising performance, an approach to leverage a small subset of fully annotated samples, and a prototype with explainable predictions, active learning features and parallelisation. Noting some problems in the literature, this study is conducted with one of the largest WSI colorectal samples dataset with approximately 10,500 WSIs. Of these samples, 900 are testing samples. Furthermore, the robustness of the proposed method is assessed with two additional external datasets (TCGA and PAIP) and a dataset of samples collected directly from the proposed prototype. Our proposed method predicts, for the patch-based tiles, a class based on the severity of the dysplasia and uses that information to classify the whole slide. It is trained with an interpretable mixed-supervision scheme to leverage the domain knowledge introduced by pathologists through spatial annotations. The mixed-supervision scheme allowed for an intelligent sampling strategy effectively evaluated in several different scenarios without compromising the performance. On the internal dataset, the method shows an accuracy of 93.44% and a sensitivity between positive (low-grade and high-grade dysplasia) and non-neoplastic samples of 0.996. On the external test samples varied with TCGA being the most challenging dataset with an overall accuracy of 84.91% and a sensitivity of 0.996.
Language	English
Publishing date	2024-03-05
Publishing country	England
Document type	Journal Article
ISSN	2397-768X
ISSN	2397-768X
DOI	10.1038/s41698-024-00539-4
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Article: iMIL4PATH: A Semi-Supervised Interpretable Approach for Colorectal Whole-Slide Images.

Neto, Pedro C / Oliveira, Sara P / Montezuma, Diana / Fraga, João / Monteiro, Ana / Ribeiro, Liliana / Gonçalves, Sofia / Pinto, Isabel M / Cardoso, Jaime S

Cancers

2022 Volume 14, Issue 10

Abstract: Colorectal cancer (CRC) diagnosis is based on samples obtained from biopsies, assessed in pathology laboratories. Due to population growth and ageing, as well as better screening programs, the CRC incidence rate has been increasing, leading to a higher ... ...

Abstract	Colorectal cancer (CRC) diagnosis is based on samples obtained from biopsies, assessed in pathology laboratories. Due to population growth and ageing, as well as better screening programs, the CRC incidence rate has been increasing, leading to a higher workload for pathologists. In this sense, the application of AI for automatic CRC diagnosis, particularly on whole-slide images (WSI), is of utmost relevance, in order to assist professionals in case triage and case review. In this work, we propose an interpretable semi-supervised approach to detect lesions in colorectal biopsies with high sensitivity, based on multiple-instance learning and feature aggregation methods. The model was developed on an extended version of the recent, publicly available CRC dataset (the CRC+ dataset with 4433 WSI), using 3424 slides for training and 1009 slides for evaluation. The proposed method attained 90.19% classification ACC, 98.8% sensitivity, 85.7% specificity, and a quadratic weighted kappa of 0.888 at slide-based evaluation. Its generalisation capabilities are also studied on two publicly available external datasets.
Language	English
Publishing date	2022-05-18
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2527080-1
ISSN	2072-6694
ISSN	2072-6694
DOI	10.3390/cancers14102489
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Article ; Online: A CAD system for automatic dysplasia grading on H&E cervical whole-slide images.

Oliveira, Sara P / Montezuma, Diana / Moreira, Ana / Oliveira, Domingos / Neto, Pedro C / Monteiro, Ana / Monteiro, João / Ribeiro, Liliana / Gonçalves, Sofia / Pinto, Isabel M / Cardoso, Jaime S

Scientific reports

2023 Volume 13, Issue 1, Page(s) 3970

Abstract: Cervical cancer is the fourth most common female cancer worldwide and the fourth leading cause of cancer-related death in women. Nonetheless, it is also among the most successfully preventable and treatable types of cancer, provided it is early ... ...

Abstract	Cervical cancer is the fourth most common female cancer worldwide and the fourth leading cause of cancer-related death in women. Nonetheless, it is also among the most successfully preventable and treatable types of cancer, provided it is early identified and properly managed. As such, the detection of pre-cancerous lesions is crucial. These lesions are detected in the squamous epithelium of the uterine cervix and are graded as low- or high-grade intraepithelial squamous lesions, known as LSIL and HSIL, respectively. Due to their complex nature, this classification can become very subjective. Therefore, the development of machine learning models, particularly directly on whole-slide images (WSI), can assist pathologists in this task. In this work, we propose a weakly-supervised methodology for grading cervical dysplasia, using different levels of training supervision, in an effort to gather a bigger dataset without the need of having all samples fully annotated. The framework comprises an epithelium segmentation step followed by a dysplasia classifier (non-neoplastic, LSIL, HSIL), making the slide assessment completely automatic, without the need for manual identification of epithelial areas. The proposed classification approach achieved a balanced accuracy of 71.07% and sensitivity of 72.18%, at the slide-level testing on 600 independent samples, which are publicly available upon reasonable request.
MeSH term(s)	Female ; Humans ; Cervix Uteri/diagnostic imaging ; Cervix Uteri/pathology ; Uterine Cervical Dysplasia/pathology ; Uterine Cervical Neoplasms/diagnosis ; Hyperplasia/pathology ; Squamous Intraepithelial Lesions/pathology ; Carcinoma, Squamous Cell/pathology ; Neoplasm Grading
Language	English
Publishing date	2023-03-09
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-023-30497-z
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Article ; Online: Quality Control in Digital Pathology: Automatic Fragment Detection and Counting.

Albuquerque, Tome / Moreira, Ana / Barros, Beatriz / Montezuma, Diana / Oliveira, Sara P / Neto, Pedro C / Monteiro, Joao / Ribeiro, Liliana / Goncalves, Sofia / Monteiro, Ana / Pinto, Isabel M / Cardoso, Jaime S

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

2022 Volume 2022, Page(s) 588–593

Abstract: Manual assessment of fragments during the pro-cessing of pathology specimens is critical to ensure that the material available for slide analysis matches that captured during grossing without losing valuable material during this process. However, this ... ...

Abstract	Manual assessment of fragments during the pro-cessing of pathology specimens is critical to ensure that the material available for slide analysis matches that captured during grossing without losing valuable material during this process. However, this step is still performed manually, resulting in lost time and delays in making the complete case available for evaluation by the pathologist. To overcome this limitation, we developed an autonomous system that can detect and count the number of fragments contained on each slide. We applied and compared two different methods: conventional machine learning methods and deep convolutional network methods. For conventional machine learning methods, we tested a two-stage approach with a supervised classifier followed by unsupervised hierarchical clustering. In addition, Fast R-CNN and YOLOv5, two state-of-the-art deep learning models for detection, were used and compared. All experiments were performed on a dataset comprising 1276 images of colorec-tal biopsy and polypectomy specimens manually labeled for fragment/set detection. The best results were obtained with the YOLOv5 architecture with a map@0.5 of 0.977 for fragment/set detection.
MeSH term(s)	Biopsy ; Machine Learning ; Neural Networks, Computer ; Quality Control
Language	English
Publishing date	2022-09-09
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	2694-0604
ISSN (online)	2694-0604
DOI	10.1109/EMBC48229.2022.9871208
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Book ; Online: A CAD System for Colorectal Cancer from WSI

Neto, Pedro C. / Montezuma, Diana / Oliveira, Sara P. / Oliveira, Domingos / Fraga, João / Monteiro, Ana / Monteiro, João / Ribeiro, Liliana / Gonçalves, Sofia / Reinhard, Stefan / Zlobec, Inti / Pinto, Isabel M. / Cardoso, Jaime S.

A Clinically Validated Interpretable ML-based Prototype

2023

Abstract: The integration of Artificial Intelligence (AI) and Digital Pathology has been increasing over the past years. Nowadays, applications of deep learning (DL) methods to diagnose cancer from whole-slide images (WSI) are, more than ever, a reality within ... ...

Abstract	The integration of Artificial Intelligence (AI) and Digital Pathology has been increasing over the past years. Nowadays, applications of deep learning (DL) methods to diagnose cancer from whole-slide images (WSI) are, more than ever, a reality within different research groups. Nonetheless, the development of these systems was limited by a myriad of constraints regarding the lack of training samples, the scaling difficulties, the opaqueness of DL methods, and, more importantly, the lack of clinical validation. As such, we propose a system designed specifically for the diagnosis of colorectal samples. The construction of such a system consisted of four stages: (1) a careful data collection and annotation process, which resulted in one of the largest WSI colorectal samples datasets; (2) the design of an interpretable mixed-supervision scheme to leverage the domain knowledge introduced by pathologists through spatial annotations; (3) the development of an effective sampling approach based on the expected severeness of each tile, which decreased the computation cost by a factor of almost 6x; (4) the creation of a prototype that integrates the full set of features of the model to be evaluated in clinical practice. During these stages, the proposed method was evaluated in four separate test sets, two of them are external and completely independent. On the largest of those sets, the proposed approach achieved an accuracy of 93.44%. DL for colorectal samples is a few steps closer to stop being research exclusive and to become fully integrated in clinical practice. Comment: Under Review
Keywords	Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Computer Vision and Pattern Recognition
Subject code	004
Publishing date	2023-01-06
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: CAD systems for colorectal cancer from WSI are still not ready for clinical acceptance.

Oliveira, Sara P / Neto, Pedro C / Fraga, João / Montezuma, Diana / Monteiro, Ana / Monteiro, João / Ribeiro, Liliana / Gonçalves, Sofia / Pinto, Isabel M / Cardoso, Jaime S

Scientific reports

2021 Volume 11, Issue 1, Page(s) 14358

Abstract: Most oncological cases can be detected by imaging techniques, but diagnosis is based on pathological assessment of tissue samples. In recent years, the pathology field has evolved to a digital era where tissue samples are digitised and evaluated on ... ...

Abstract	Most oncological cases can be detected by imaging techniques, but diagnosis is based on pathological assessment of tissue samples. In recent years, the pathology field has evolved to a digital era where tissue samples are digitised and evaluated on screen. As a result, digital pathology opened up many research opportunities, allowing the development of more advanced image processing techniques, as well as artificial intelligence (AI) methodologies. Nevertheless, despite colorectal cancer (CRC) being the second deadliest cancer type worldwide, with increasing incidence rates, the application of AI for CRC diagnosis, particularly on whole-slide images (WSI), is still a young field. In this review, we analyse some relevant works published on this particular task and highlight the limitations that hinder the application of these works in clinical practice. We also empirically investigate the feasibility of using weakly annotated datasets to support the development of computer-aided diagnosis systems for CRC from WSI. Our study underscores the need for large datasets in this field and the use of an appropriate learning methodology to gain the most benefit from partially annotated datasets. The CRC WSI dataset used in this study, containing 1,133 colorectal biopsy and polypectomy samples, is available upon reasonable request.
MeSH term(s)	Adenoma/diagnosis ; Algorithms ; Artificial Intelligence ; Biomedical Engineering/methods ; Biopsy ; Colorectal Neoplasms/diagnosis ; Computational Biology/methods ; Diagnosis, Computer-Assisted/instrumentation ; Diagnosis, Computer-Assisted/methods ; Diagnosis, Computer-Assisted/trends ; Diagnostic Imaging/instrumentation ; Diagnostic Imaging/trends ; Feasibility Studies ; Humans ; Image Interpretation, Computer-Assisted/methods ; Image Processing, Computer-Assisted/methods ; Learning ; Machine Learning ; Software
Language	English
Publishing date	2021-07-13
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Review
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-021-93746-z
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Article ; Online: A Regression Model for Predicting Shape Deformation after Breast Conserving Surgery.

Zolfagharnasab, Hooshiar / Bessa, Sílvia / Oliveira, Sara P / Faria, Pedro / Teixeira, João F / Cardoso, Jaime S / Oliveira, Hélder P

Sensors (Basel, Switzerland)

2018 Volume 18, Issue 1

Abstract: Breast cancer treatments can have a negative impact on breast aesthetics, in case when surgery is intended to intersect tumor. For many years mastectomy was the only surgical option, but more recently breast conserving surgery (BCS) has been promoted as ... ...

Abstract	Breast cancer treatments can have a negative impact on breast aesthetics, in case when surgery is intended to intersect tumor. For many years mastectomy was the only surgical option, but more recently breast conserving surgery (BCS) has been promoted as a liable alternative to treat cancer while preserving most part of the breast. However, there is still a significant number of BCS intervened patients who are unpleasant with the result of the treatment, which leads to self-image issues and emotional overloads. Surgeons recognize the value of a tool to predict the breast shape after BCS to facilitate surgeon/patient communication and allow more educated decisions; however, no such tool is available that is suited for clinical usage. These tools could serve as a way of visually sensing the aesthetic consequences of the treatment. In this research, it is intended to propose a methodology for predict the deformation after BCS by using machine learning techniques. Nonetheless, there is no appropriate dataset containing breast data before and after surgery in order to train a learning model. Therefore, an in-house semi-synthetic dataset is proposed to fulfill the requirement of this research. Using the proposed dataset, several learning methodologies were investigated, and promising outcomes are obtained.
MeSH term(s)	Breast ; Breast Neoplasms ; Humans ; Mastectomy ; Mastectomy, Segmental
Language	English
Publishing date	2018-01-09
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2052857-7
ISSN	1424-8220 ; 1424-8220
ISSN (online)	1424-8220
ISSN	1424-8220
DOI	10.3390/s18010167
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Article ; Online: Three-Dimensional Planning Tool for Breast Conserving Surgery: A Technological Review.

Oliveira, Sara P / Morgado, Pedro / Gouveia, Pedro F / Teixeira, João F / Bessa, Silvia / Monteiro, João P / Zolfagharnasab, Hooshiar / Reis, Marta / Silva, Nuno L / Veiga, Diana / Cardoso, Maria J / Oliveira, Helder P / Ferreira, Manuel João

Critical reviews in biomedical engineering

2019 Volume 46, Issue 6, Page(s) 523–580

Abstract: Breast cancer is one of the most common malignancies affecting women worldwide. However, despite its incidence trends have increased, the mortality rate has significantly decreased. The primary concern in any cancer treatment is the oncological outcome ... ...

Abstract	Breast cancer is one of the most common malignancies affecting women worldwide. However, despite its incidence trends have increased, the mortality rate has significantly decreased. The primary concern in any cancer treatment is the oncological outcome but, in the case of breast cancer, the surgery aesthetic result has become an important quality indicator for breast cancer patients. In this sense, an adequate surgical planning and prediction tool would empower the patient regarding the treatment decision process, enabling a better communication between the surgeon and the patient and a better understanding of the impact of each surgical option. To develop such tool, it is necessary to create complete 3D model of the breast, integrating both inner and outer breast data. In this review, we thoroughly explore and review the major existing works that address, directly or not, the technical challenges involved in the development of a 3D software planning tool in the field of breast conserving surgery.
MeSH term(s)	Breast/diagnostic imaging ; Breast/pathology ; Breast Neoplasms/diagnosis ; Breast Neoplasms/surgery ; Female ; Humans ; Imaging, Three-Dimensional/methods ; Mastectomy, Segmental/methods ; Patient Care Planning ; Preoperative Period ; Software
Language	English
Publishing date	2019-02-25
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Review
ZDB-ID	1411103-2
ISSN	1943-619X ; 0278-940X
ISSN (online)	1943-619X
ISSN	0278-940X
DOI	10.1615/CritRevBiomedEng.2018028476
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