Whole-slide-imaging Cancer Metastases Detection and Localization with Limited Tumorous Data

• URL: http://arxiv.org/abs/2303.10342v1
• Date: Sat, 18 Mar 2023 06:07:10 GMT
• Title: Whole-slide-imaging Cancer Metastases Detection and Localization with Limited Tumorous Data
• Authors: Yinsheng He and Xingyu Li
• Abstract summary: We tackle the tumor localization and detection problem under the setting of few labeled whole slide images. Our method achieves similar performance by less than ten percent of training samples on the public Camelyon16 dataset.
• Score: 2.715884199292287
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, various deep learning methods have shown significant successes in medical image analysis, especially in the detection of cancer metastases in hematoxylin and eosin (H&E) stained whole-slide images (WSIs). However, in order to obtain good performance, these research achievements rely on hundreds of well-annotated WSIs. In this study, we tackle the tumor localization and detection problem under the setting of few labeled whole slide images and introduce a patch-based analysis pipeline based on the latest reverse knowledge distillation architecture. To address the extremely unbalanced normal and tumorous samples in training sample collection, we applied the focal loss formula to the representation similarity metric for model optimization. Compared with prior arts, our method achieves similar performance by less than ten percent of training samples on the public Camelyon16 dataset. In addition, this is the first work that show the great potential of the knowledge distillation models in computational histopathology.

Related papers

• Towards a Comprehensive Benchmark for Pathological Lymph Node Metastasis in Breast Cancer Sections [21.75452517154339]
  We reprocessed 1,399 whole slide images (WSIs) and labels from the Camelyon-16 and Camelyon-17 datasets. Based on the sizes of re-annotated tumor regions, we upgraded the binary cancer screening task to a four-class task.
  arXiv  Detail & Related papers  (2024-11-16T09:19:24Z)
• Evaluating the plausibility of synthetic images for improving automated endoscopic stone recognition [0.9480662172227129]
  Currently, the Morpho-Constitutional Analysis (MCA) is the de facto approach for the etiological diagnosis of kidney stone formation. More recently, research has focused on performing such tasks intra-operatively, an approach known as Endoscopic Stone Recognition (ESR)
  arXiv  Detail & Related papers  (2024-09-20T11:19:08Z)
• Rapid hyperspectral photothermal mid-infrared spectroscopic imaging from sparse data for gynecologic cancer tissue subtyping [3.550171634694342]
  Mid-infrared (MIR) hyperspectral photothermal imaging is a label-free, biochemically quantitative technology. This work presents a novel approach to MIR photothermal imaging that enhances its speed by an order of magnitude.
  arXiv  Detail & Related papers  (2024-02-28T00:57:35Z)
• Realistic Data Enrichment for Robust Image Segmentation in Histopathology [2.248423960136122]
  We propose a new approach, based on diffusion models, which can enrich an imbalanced dataset with plausible examples from underrepresented groups. Our method can simply expand limited clinical datasets making them suitable to train machine learning pipelines.
  arXiv  Detail & Related papers  (2023-04-19T09:52:50Z)
• Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective [51.70661197256033]
  We propose ARCO, a semi-supervised contrastive learning framework with stratified group theory for medical image segmentation. We first propose building ARCO through the concept of variance-reduced estimation and show that certain variance-reduction techniques are particularly beneficial in pixel/voxel-level segmentation tasks. We experimentally validate our approaches on eight benchmarks, i.e., five 2D/3D medical and three semantic segmentation datasets, with different label settings.
  arXiv  Detail & Related papers  (2023-02-03T13:50:25Z)
• Orientation-Shared Convolution Representation for CT Metal Artifact Learning [63.67718355820655]
  During X-ray computed tomography (CT) scanning, metallic implants carrying with patients often lead to adverse artifacts. Existing deep-learning-based methods have gained promising reconstruction performance. We propose an orientation-shared convolution representation strategy to adapt the physical prior structures of artifacts.
  arXiv  Detail & Related papers  (2022-12-26T13:56:12Z)
• Automated Segmentation of Computed Tomography Images with Submanifold Sparse Convolutional Networks [2.064612766965483]
  We propose a process of sparsification of the input images and submanifold sparse convolutional networks as an alternative to downsampling. As a proof of concept, we applied this new methodology to Computed Tomography images of renal cancer patients.
  arXiv  Detail & Related papers  (2022-12-06T09:47:52Z)
• Stain-invariant self supervised learning for histopathology image analysis [74.98663573628743]
  We present a self-supervised algorithm for several classification tasks within hematoxylin and eosin stained images of breast cancer. Our method achieves the state-of-the-art performance on several publicly available breast cancer datasets.
  arXiv  Detail & Related papers  (2022-11-14T18:16:36Z)
• RGMIM: Region-Guided Masked Image Modeling for Learning Meaningful Representations from X-Ray Images [49.24576562557866]
  We propose a novel method called region-guided masked image modeling (RGMIM) for learning meaningful representations from X-ray images. RGMIM significantly improved performance in small data volumes, such as 5% and 10% of the training set compared to other methods.
  arXiv  Detail & Related papers  (2022-11-01T07:41:03Z)
• Incremental Cross-view Mutual Distillation for Self-supervised Medical CT Synthesis [88.39466012709205]
  This paper builds a novel medical slice to increase the between-slice resolution. Considering that the ground-truth intermediate medical slices are always absent in clinical practice, we introduce the incremental cross-view mutual distillation strategy. Our method outperforms state-of-the-art algorithms by clear margins.
  arXiv  Detail & Related papers  (2021-12-20T03:38:37Z)
• Improved Slice-wise Tumour Detection in Brain MRIs by Computing Dissimilarities between Latent Representations [68.8204255655161]
  Anomaly detection for Magnetic Resonance Images (MRIs) can be solved with unsupervised methods. We have proposed a slice-wise semi-supervised method for tumour detection based on the computation of a dissimilarity function in the latent space of a Variational AutoEncoder. We show that by training the models on higher resolution images and by improving the quality of the reconstructions, we obtain results which are comparable with different baselines.
  arXiv  Detail & Related papers  (2020-07-24T14:02:09Z)

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.