Evaluation of Multi-Scale Multiple Instance Learning to Improve Thyroid Cancer Classification

• URL: http://arxiv.org/abs/2204.10942v1
• Date: Fri, 22 Apr 2022 21:48:56 GMT
• Title: Evaluation of Multi-Scale Multiple Instance Learning to Improve Thyroid Cancer Classification
• Authors: Maximilian E. Tschuchnig, Philipp Grubm\"uller, Lea M. Stangassinger, Christina Kreutzer, S\'ebastien Couillard-Despr\'es, Gertie J. Oostingh, Anton Hittmair, Michael Gadermayr
• Abstract summary: Thyroid cancer is the fifth most common malignancy diagnosed in women. differentiation of cancer sub-types is important for treatment and current, automatic computer-aided differentiation of cancer types is crucial. Patch based multiple instance learning approaches, combined with aggregations such as bag-of-words, is a common approach. This work's contribution is to extend a patch based state-of-the-art method by generating and combining feature vectors of three different patch resolutions.
• Score: 0.3518016233072556
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Thyroid cancer is currently the fifth most common malignancy diagnosed in women. Since differentiation of cancer sub-types is important for treatment and current, manual methods are time consuming and subjective, automatic computer-aided differentiation of cancer types is crucial. Manual differentiation of thyroid cancer is based on tissue sections, analysed by pathologists using histological features. Due to the enormous size of gigapixel whole slide images, holistic classification using deep learning methods is not feasible. Patch based multiple instance learning approaches, combined with aggregations such as bag-of-words, is a common approach. This work's contribution is to extend a patch based state-of-the-art method by generating and combining feature vectors of three different patch resolutions and analysing three distinct ways of combining them. The results showed improvements in one of the three multi-scale approaches, while the others led to decreased scores. This provides motivation for analysis and discussion of the individual approaches.

Related papers

• Adaptive Deep Learning for Multiclass Breast Cancer Classification via Misprediction Risk Analysis [0.8028869343053783]
  Early detection is crucial for improving patient outcomes. Computer-aided diagnostic approaches have significantly enhanced breast cancer detection. However, these methods face challenges in multiclass classification, leading to frequent mispredictions.
  arXiv  Detail & Related papers  (2025-03-17T03:25:28Z)
• Pathological Prior-Guided Multiple Instance Learning For Mitigating Catastrophic Forgetting in Breast Cancer Whole Slide Image Classification [50.899861205016265]
  We propose a new framework PaGMIL to mitigate catastrophic forgetting in breast cancer WSI classification. Our framework introduces two key components into the common MIL model architecture. We evaluate the continual learning performance of PaGMIL across several public breast cancer datasets.
  arXiv  Detail & Related papers  (2025-03-08T04:51:58Z)
• DIOR-ViT: Differential Ordinal Learning Vision Transformer for Cancer Classification in Pathology Images [4.2832657904981435]
  We introduce a differential ordinal learning problem in which we define and learn the degree of difference in the categorical class labels between pairs of samples. We demonstrate that the adoption of differential ordinal learning can improve the accuracy and reliability of cancer grading. The proposed approach should be applicable to other diseases and problems as they involve ordinal relationship among class labels.
  arXiv  Detail & Related papers  (2024-07-10T12:42:27Z)
• A transductive few-shot learning approach for classification of digital histopathological slides from liver cancer [0.0]
  This paper presents a new approach for classifying 2D histopathology patches using few-shot learning. By applying a sliding window technique to histopathology slides, we illustrate the practical benefits of transductive learning.
  arXiv  Detail & Related papers  (2023-11-29T15:44:00Z)
• 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)
• A Pathologist-Informed Workflow for Classification of Prostate Glands in Histopathology [62.997667081978825]
  Pathologists diagnose and grade prostate cancer by examining tissue from needle biopsies on glass slides. Cancer's severity and risk of metastasis are determined by the Gleason grade, a score based on the organization and morphology of prostate cancer glands. This paper proposes an automated workflow that follows pathologists' textitmodus operandi, isolating and classifying multi-scale patches of individual glands.
  arXiv  Detail & Related papers  (2022-09-27T14:08:19Z)
• Breast Cancer Classification Based on Histopathological Images Using a Deep Learning Capsule Network [0.0]
  This study aims to classify different types of breast cancer using histological images (HIs) We present an enhanced capsule network that extracts multi-scale features using the Res2Net block and four additional convolutional layers. As a result, the new method outperforms the old ones since it automatically learns the best possible features.
  arXiv  Detail & Related papers  (2022-08-01T03:45:36Z)
• Metastatic Cancer Outcome Prediction with Injective Multiple Instance Pooling [1.0965065178451103]
  We process two public datasets to set up a benchmark cohort of 341 patient in total for studying outcome prediction of metastatic cancer. We propose two injective multiple instance pooling functions that are better suited to outcome prediction. Our results show that multiple instance learning with injective pooling functions can achieve state-of-the-art performance in the non-small-cell lung cancer CT and head and neck CT outcome prediction benchmarking tasks.
  arXiv  Detail & Related papers  (2022-03-09T16:58:03Z)
• 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)
• Triplet Contrastive Learning for Brain Tumor Classification [99.07846518148494]
  We present a novel approach of directly learning deep embeddings for brain tumor types, which can be used for downstream tasks such as classification. We evaluate our method on an extensive brain tumor dataset which consists of 27 different tumor classes, out of which 13 are defined as rare.
  arXiv  Detail & Related papers  (2021-08-08T11:26:34Z)
• Weakly supervised multiple instance learning histopathological tumor segmentation [51.085268272912415]
  We propose a weakly supervised framework for whole slide imaging segmentation. We exploit a multiple instance learning scheme for training models. The proposed framework has been evaluated on multi-locations and multi-centric public data from The Cancer Genome Atlas and the PatchCamelyon dataset.
  arXiv  Detail & Related papers  (2020-04-10T13:12:47Z)
• 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.