DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classification

Zhang, Hongrun; Meng, Yanda; Zhao, Yitian; Qiao, Yihong; Yang, Xiaoyun; Coupland, Sarah E.; Zheng, Yalin

doi:10.1109/cvpr52688.2022.01824

Cited by 125 publications

(97 citation statements)

References 34 publications

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“…This was notable at the time, as previous methods had relied on tediously annotated tissue ROIs. Several studies have since followed suit [ 4 , 17 , 20 , 25 , 67 , 68 , 69 , 70 ].…”

Section: Related Workmentioning

confidence: 99%

“…Courtiol et al [ 65 ] were the first to apply MIL to subtype cancer using WSIs—specifically, subtyping NSCLC into LUAD and LUSC using WSIs available from TCGA [ 71 ]—and drew similar conclusions and implications from their work with Camelyon16. The standard set by Courtiol et al has endured through subsequent studies [ 17 , 25 , 68 , 69 , 70 , 72 , 73 , 74 ]. Wang et al added an addition subtype for small-cell lung cancer (SCLC) [ 43 ] using an in-house dataset.…”

Section: Related Workmentioning

confidence: 99%

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Contrastive Multiple Instance Learning: An Unsupervised Framework for Learning Slide-Level Representations of Whole Slide Histopathology Images without Labels

Tavolara

Gürcan

Niazi

2022

Cancers

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Recent methods in computational pathology have trended towards semi- and weakly-supervised methods requiring only slide-level labels. Yet, even slide-level labels may be absent or irrelevant to the application of interest, such as in clinical trials. Hence, we present a fully unsupervised method to learn meaningful, compact representations of WSIs. Our method initially trains a tile-wise encoder using SimCLR, from which subsets of tile-wise embeddings are extracted and fused via an attention-based multiple-instance learning framework to yield slide-level representations. The resulting set of intra-slide-level and inter-slide-level embeddings are attracted and repelled via contrastive loss, respectively. This resulted in slide-level representations with self-supervision. We applied our method to two tasks— (1) non-small cell lung cancer subtyping (NSCLC) as a classification prototype and (2) breast cancer proliferation scoring (TUPAC16) as a regression prototype—and achieved an AUC of 0.8641 ± 0.0115 and correlation (R2) of 0.5740 ± 0.0970, respectively. Ablation experiments demonstrate that the resulting unsupervised slide-level feature space can be fine-tuned with small datasets for both tasks. Overall, our method approaches computational pathology in a novel manner, where meaningful features can be learned from whole-slide images without the need for annotations of slide-level labels. The proposed method stands to benefit computational pathology, as it theoretically enables researchers to benefit from completely unlabeled whole-slide images.

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“…This was notable at the time, as previous methods had relied on tediously annotated tissue ROIs. Several studies have since followed suit [ 4 , 17 , 20 , 25 , 67 , 68 , 69 , 70 ].…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Contrastive Multiple Instance Learning: An Unsupervised Framework for Learning Slide-Level Representations of Whole Slide Histopathology Images without Labels

Tavolara

Gürcan

Niazi

2022

Cancers

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“…The core of MIL algorithms is to associate the slide-level label (e.g., normal specimen or cancerous specimen) with patchlevel features. MIL-based WSI analysis has the potential to improve diagnostic accuracy and has been well studied in histopathology [138,139].…”

Section: Whole Slide Image Analysismentioning

confidence: 99%

A Survey on Deep Learning-based Cervical Cytology Screening: from Cell Identification to Whole Slide Image Analysis

Jiang

Shen

et al. 2023

Preprint

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Cervical cancer is one of the most common cancers in daily life. Early detection and diagnosis can effectively help facilitate subsequent clinical treatment and management. With the growing advancement of artificial intelligence (AI) and deep learning (DL) techniques, an increasing number of computer-aided diagnosis (CAD) methods based on deep learning have been applied in cervical cytology screening. In this paper, we survey more than 70 publications since 2016 to provide a systematic and comprehensive review of DL-based cervical cytology screening. First, we provide a concise summary of the medical and biological knowledge pertaining to cervical cytology, since we hold a firm belief that a comprehensive biomedical understanding can significantly contribute to the development of CAD systems. Then, we collect a wide range of public cervical cytology datasets. Besides, image analysis approaches and applications including cervical cell identification, abnormal cell or area detection, cell region segmentation and cervical whole slide image diagnosis are summarized. Finally, we discuss the present obstacles and promising directions for future researches in automated cervical cytology screening.

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“…Many approaches have been proposed so far for the processing and analysis of WSIs. Recent approaches use Vision Transformers and Multiple Instance Learning (e.g., [24], [25], to quote a few). A review of such approaches is beyond the scope of this paper.…”

Section: Computational Pathologymentioning

confidence: 99%

Whole Slide Image Quality in Digital Pathology: Review and Perspectives

Brixtel¹,

Bougleux

Lézoray

et al. 2022

IEEE Access

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With the advent of whole slide image (WSI) scanners, pathology is undergoing a digital revolution. Simultaneously, with the development of image analysis algorithms based on artificial intelligence tools, the application of computerized WSI analysis can now be expected. However, transferring such tools into clinical practice is very challenging as they must deal with many artifacts that can occur during sample preparation and digitization. Therefore, the quality of WSIs is of prime importance, and we propose a review of the state-of-the-art of computational approaches for quality control. In particular, we focus on WSI quality issues related to the presence of sample preparation artifacts, compression artifacts, color variations, and out-of-focus areas. An analysis of the monthly WSI clinical routine in a cytological laboratory confirms the importance of implementing quality control measures. Given this observation, we draw perspectives on how a computational quality process can be included in a computational pathology diagnosis pipeline.

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DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classification

Cited by 125 publications

References 34 publications

Contrastive Multiple Instance Learning: An Unsupervised Framework for Learning Slide-Level Representations of Whole Slide Histopathology Images without Labels

Contrastive Multiple Instance Learning: An Unsupervised Framework for Learning Slide-Level Representations of Whole Slide Histopathology Images without Labels

A Survey on Deep Learning-based Cervical Cytology Screening: from Cell Identification to Whole Slide Image Analysis

Whole Slide Image Quality in Digital Pathology: Review and Perspectives

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