Multi-scale Domain-adversarial Multiple-instance CNN for Cancer Subtype Classification with Unannotated Histopathological Images

• URL: http://arxiv.org/abs/2001.01599v2
• Date: Thu, 2 Apr 2020 08:03:24 GMT
• Title: Multi-scale Domain-adversarial Multiple-instance CNN for Cancer Subtype Classification with Unannotated Histopathological Images
• Authors: Noriaki Hashimoto, Daisuke Fukushima, Ryoichi Koga, Yusuke Takagi, Kaho Ko, Kei Kohno, Masato Nakaguro, Shigeo Nakamura, Hidekata Hontani and Ichiro Takeuchi
• Abstract summary: We develop a new CNN-based cancer subtype classification method by effectively combining multiple-instance, domain adversarial, and multi-scale learning frameworks. The classification performance was significantly better than the standard CNN or other conventional methods, and the accuracy compared favorably with that of standard pathologists.
• Score: 16.02231907106384
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a new method for cancer subtype classification from histopathological images, which can automatically detect tumor-specific features in a given whole slide image (WSI). The cancer subtype should be classified by referring to a WSI, i.e., a large-sized image (typically 40,000x40,000 pixels) of an entire pathological tissue slide, which consists of cancer and non-cancer portions. One difficulty arises from the high cost associated with annotating tumor regions in WSIs. Furthermore, both global and local image features must be extracted from the WSI by changing the magnifications of the image. In addition, the image features should be stably detected against the differences of staining conditions among the hospitals/specimens. In this paper, we develop a new CNN-based cancer subtype classification method by effectively combining multiple-instance, domain adversarial, and multi-scale learning frameworks in order to overcome these practical difficulties. When the proposed method was applied to malignant lymphoma subtype classifications of 196 cases collected from multiple hospitals, the classification performance was significantly better than the standard CNN or other conventional methods, and the accuracy compared favorably with that of standard pathologists.

Related papers

• Finding Regions of Interest in Whole Slide Images Using Multiple Instance Learning [0.23301643766310368]
  Whole Slide Images (WSI) represent a particular challenge to AI-based/AI-mediated analysis because pathology labeling is typically done at slide-level, instead of tile-level. We propose a weakly supervised Multiple Instance Learning (MIL) approach to accurately predict the overall cancer phenotype.
  arXiv  Detail & Related papers  (2024-04-01T19:33:41Z)
• RACR-MIL: Weakly Supervised Skin Cancer Grading using Rank-Aware Contextual Reasoning on Whole Slide Images [1.7625232415232948]
  Cutaneous squamous cell cancer (cSCC) is the second most common skin cancer in the US. We propose an automated weakly-supervised grading approach for c SCC WSIs. The proposed model transforms each WSI into a bag of tiled patches and leverages attention-based multiple-instance learning to assign a WSI-level grade.
  arXiv  Detail & Related papers  (2023-08-29T20:25:49Z)
• CAMIL: Context-Aware Multiple Instance Learning for Cancer Detection and Subtyping in Whole Slide Images [3.1118773046912382]
  We propose the Context-Aware Multiple Instance Learning (CAMIL) architecture for cancer diagnosis. CAMIL incorporates neighbor-constrained attention to consider dependencies among tiles within a Whole Slide Images (WSI) and integrates contextual constraints as prior knowledge. We evaluate CAMIL on subtyping non-small cell lung cancer (TCGA-NSCLC) and detecting lymph node metastasis, achieving test AUCs of 97.5%, 95.9%, and 88.1%, respectively.
  arXiv  Detail & Related papers  (2023-05-09T10:06:37Z)
• Cross-modulated Few-shot Image Generation for Colorectal Tissue Classification [58.147396879490124]
  Our few-shot generation method, named XM-GAN, takes one base and a pair of reference tissue images as input and generates high-quality yet diverse images. To the best of our knowledge, we are the first to investigate few-shot generation in colorectal tissue images.
  arXiv  Detail & Related papers  (2023-04-04T17:50:30Z)
• Multi-Scale Input Strategies for Medulloblastoma Tumor Classification using Deep Transfer Learning [59.30734371401316]
  Medulloblastoma is the most common malignant brain cancer among children. CNN has shown promising results for MB subtype classification. We study the impact of tile size and input strategy.
  arXiv  Detail & Related papers  (2021-09-14T09:42:37Z)
• Wide & Deep neural network model for patch aggregation in CNN-based prostate cancer detection systems [51.19354417900591]
  Prostate cancer (PCa) is one of the leading causes of death among men, with almost 1.41 million new cases and around 375,000 deaths in 2020. To perform an automatic diagnosis, prostate tissue samples are first digitized into gigapixel-resolution whole-slide images. Small subimages called patches are extracted and predicted, obtaining a patch-level classification.
  arXiv  Detail & Related papers  (2021-05-20T18:13:58Z)
• Selecting Regions of Interest in Large Multi-Scale Images for Cancer Pathology [0.0]
  High resolution scans of microscopy slides offer enough information for a cancer pathologist to come to a conclusion regarding cancer presence, subtype, and severity based on measurements of features within the slide image at multiple scales and resolutions. We explore approaches based on Reinforcement Learning and Beam Search to learn to progressively zoom into the WSI to detect Regions of Interest (ROIs) in liver pathology slides containing one of two types of liver cancer, namely Hepatocellular Carcinoma (HCC) and Cholangiocarcinoma (CC) These ROIs can then be presented directly to the pathologist to aid in measurement and diagnosis or be used
  arXiv  Detail & Related papers  (2020-07-03T15:27:41Z)
• Spectral-Spatial Recurrent-Convolutional Networks for In-Vivo Hyperspectral Tumor Type Classification [49.32653090178743]
  We demonstrate the feasibility of in-vivo tumor type classification using hyperspectral imaging and deep learning. Our best model achieves an AUC of 76.3%, significantly outperforming previous conventional and deep learning methods.
  arXiv  Detail & Related papers  (2020-07-02T12:00:53Z)
• Synergistic Learning of Lung Lobe Segmentation and Hierarchical Multi-Instance Classification for Automated Severity Assessment of COVID-19 in CT Images [61.862364277007934]
  We propose a synergistic learning framework for automated severity assessment of COVID-19 in 3D CT images. A multi-task deep network (called M$2$UNet) is then developed to assess the severity of COVID-19 patients. Our M$2$UNet consists of a patch-level encoder, a segmentation sub-network for lung lobe segmentation, and a classification sub-network for severity assessment.
  arXiv  Detail & Related papers  (2020-05-08T03:16:15Z)
• Breast Cancer Histopathology Image Classification and Localization using Multiple Instance Learning [2.4178424543973267]
  Computer-aided pathology to analyze microscopic histopathology images for diagnosis can bring the cost and delays of diagnosis down. Deep learning in histopathology has attracted attention over the last decade of achieving state-of-the-art performance in classification and localization tasks. We present classification and localization results on two publicly available BreakHIS and BACH dataset.
  arXiv  Detail & Related papers  (2020-02-16T10:29:16Z)
• Stan: Small tumor-aware network for breast ultrasound image segmentation [68.8204255655161]
  We propose a novel deep learning architecture called Small Tumor-Aware Network (STAN) to improve the performance of segmenting tumors with different size. The proposed approach outperformed the state-of-the-art approaches in segmenting small breast tumors.
  arXiv  Detail & Related papers  (2020-02-03T22:25:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.