Cross-attention-based saliency inference for predicting cancer metastasis on whole slide images

• URL: http://arxiv.org/abs/2309.09412v1
• Date: Mon, 18 Sep 2023 00:56:19 GMT
• Title: Cross-attention-based saliency inference for predicting cancer metastasis on whole slide images
• Authors: Ziyu Su, Mostafa Rezapour, Usama Sajjad, Shuo Niu, Metin Nafi Gurcan, Muhammad Khalid Khan Niazi
• Abstract summary: Cross-attention-based salient instance inference MIL (CASiiMIL) is proposed to identify breast cancer lymph node micro-metastasis on whole slide images. We introduce a negative representation learning algorithm to facilitate the learning of saliency-informed attention weights for improved sensitivity on tumor WSIs. The proposed model outperforms the state-of-the-art MIL methods on two popular tumor metastasis detection datasets.
• Score: 3.7282630026096597
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although multiple instance learning (MIL) methods are widely used for automatic tumor detection on whole slide images (WSI), they suffer from the extreme class imbalance within the small tumor WSIs. This occurs when the tumor comprises only a few isolated cells. For early detection, it is of utmost importance that MIL algorithms can identify small tumors, even when they are less than 1% of the size of the WSI. Existing studies have attempted to address this issue using attention-based architectures and instance selection-based methodologies, but have not yielded significant improvements. This paper proposes cross-attention-based salient instance inference MIL (CASiiMIL), which involves a novel saliency-informed attention mechanism, to identify breast cancer lymph node micro-metastasis on WSIs without the need for any annotations. Apart from this new attention mechanism, we introduce a negative representation learning algorithm to facilitate the learning of saliency-informed attention weights for improved sensitivity on tumor WSIs. The proposed model outperforms the state-of-the-art MIL methods on two popular tumor metastasis detection datasets, and demonstrates great cross-center generalizability. In addition, it exhibits excellent accuracy in classifying WSIs with small tumor lesions. Moreover, we show that the proposed model has excellent interpretability attributed to the saliency-informed attention weights. We strongly believe that the proposed method will pave the way for training algorithms for early tumor detection on large datasets where acquiring fine-grained annotations is practically impossible.

Related papers

• Boosting Medical Image-based Cancer Detection via Text-guided Supervision from Reports [68.39938936308023]
  We propose a novel text-guided learning method to achieve highly accurate cancer detection results. Our approach can leverage clinical knowledge by large-scale pre-trained VLM to enhance generalization ability.
  arXiv  Detail & Related papers  (2024-05-23T07:03:38Z)
• Lumbar Spine Tumor Segmentation and Localization in T2 MRI Images Using AI [2.9746083684997418]
  This study introduces a novel data augmentation technique, aimed at automating spine tumor segmentation and localization through AI approaches. A Convolutional Neural Network (CNN) architecture is employed for tumor classification. 3D vertebral segmentation and labeling techniques are used to help pinpoint the exact location of the tumors in the lumbar spine. Results indicate a remarkable performance, with 99% accuracy for tumor segmentation, 98% accuracy for tumor classification, and 99% accuracy for tumor localization achieved with the proposed approach.
  arXiv  Detail & Related papers  (2024-05-07T05:55:50Z)
• Active Learning Enhances Classification of Histopathology Whole Slide Images with Attention-based Multiple Instance Learning [48.02011627390706]
  We train an attention-based MIL and calculate a confidence metric for every image in the dataset to select the most uncertain WSIs for expert annotation. With a novel attention guiding loss, this leads to an accuracy boost of the trained models with few regions annotated for each class. It may in the future serve as an important contribution to train MIL models in the clinically relevant context of cancer classification in histopathology.
  arXiv  Detail & Related papers  (2023-03-02T15:18:58Z)
• Attention2Minority: A salient instance inference-based multiple instance learning for classifying small lesions in whole slide images [0.0]
  We propose salient instance inference MIL (SiiMIL), a weakly-supervised MIL model for WSI classification. Our experiments imply that SiiMIL can accurately identify tumor instances, which could only take up less than 1% of a WSI.
  arXiv  Detail & Related papers  (2023-01-18T18:42:59Z)
• EMT-NET: Efficient multitask network for computer-aided diagnosis of breast cancer [58.720142291102135]
  We propose an efficient and light-weighted learning architecture to classify and segment breast tumors simultaneously. We incorporate a segmentation task into a tumor classification network, which makes the backbone network learn representations focused on tumor regions. The accuracy, sensitivity, and specificity of tumor classification is 88.6%, 94.1%, and 85.3%, respectively.
  arXiv  Detail & Related papers  (2022-01-13T05:24:40Z)
• Label Cleaning Multiple Instance Learning: Refining Coarse Annotations on Single Whole-Slide Images [83.7047542725469]
  Annotating cancerous regions in whole-slide images (WSIs) of pathology samples plays a critical role in clinical diagnosis, biomedical research, and machine learning algorithms development. We present a method, named Label Cleaning Multiple Instance Learning (LC-MIL), to refine coarse annotations on a single WSI without the need of external training data. Our experiments on a heterogeneous WSI set with breast cancer lymph node metastasis, liver cancer, and colorectal cancer samples show that LC-MIL significantly refines the coarse annotations, outperforming the state-of-the-art alternatives, even while learning from a single slide.
  arXiv  Detail & Related papers  (2021-09-22T15:06:06Z)
• 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)
• ESTAN: Enhanced Small Tumor-Aware Network for Breast Ultrasound Image Segmentation [0.0]
  We propose a novel deep neural network architecture, namely Enhanced Small Tumor-Aware Network (ESTAN) to accurately segment breast tumors. ESTAN introduces two encoders to extract and fuse image context information at different scales and utilizes row-column-wise kernels in the encoder to adapt to breast anatomy.
  arXiv  Detail & Related papers  (2020-09-27T16:42:59Z)
• 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.