SC-MIL: Supervised Contrastive Multiple Instance Learning for Imbalanced Classification in Pathology

• URL: http://arxiv.org/abs/2303.13405v2
• Date: Sat, 9 Sep 2023 14:22:11 GMT
• Title: SC-MIL: Supervised Contrastive Multiple Instance Learning for Imbalanced Classification in Pathology
• Authors: Dinkar Juyal, Siddhant Shingi, Syed Ashar Javed, Harshith Padigela, Chintan Shah, Anand Sampat, Archit Khosla, John Abel, Amaro Taylor-Weiner
• Abstract summary: Machine learning problems in medical imaging often deal with rare diseases. In pathology images, there is another level of imbalance, where given a positively labeled Whole Slide Image (WSI), only a fraction of pixels within it contribute to the positive label. We propose a joint-training MIL framework in the presence of label imbalance that progressively transitions from learning bag-level representations to optimal classifier learning.
• Score: 2.854576370929018
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Multiple Instance learning (MIL) models have been extensively used in pathology to predict biomarkers and risk-stratify patients from gigapixel-sized images. Machine learning problems in medical imaging often deal with rare diseases, making it important for these models to work in a label-imbalanced setting. In pathology images, there is another level of imbalance, where given a positively labeled Whole Slide Image (WSI), only a fraction of pixels within it contribute to the positive label. This compounds the severity of imbalance and makes imbalanced classification in pathology challenging. Furthermore, these imbalances can occur in out-of-distribution (OOD) datasets when the models are deployed in the real-world. We leverage the idea that decoupling feature and classifier learning can lead to improved decision boundaries for label imbalanced datasets. To this end, we investigate the integration of supervised contrastive learning with multiple instance learning (SC-MIL). Specifically, we propose a joint-training MIL framework in the presence of label imbalance that progressively transitions from learning bag-level representations to optimal classifier learning. We perform experiments with different imbalance settings for two well-studied problems in cancer pathology: subtyping of non-small cell lung cancer and subtyping of renal cell carcinoma. SC-MIL provides large and consistent improvements over other techniques on both in-distribution (ID) and OOD held-out sets across multiple imbalanced settings.

Related papers

• Cross- and Intra-image Prototypical Learning for Multi-label Disease Diagnosis and Interpretation [15.303610605543746]
  We present a novel Cross- and Intra-image Prototypical Learning framework, for accurate multi-label disease diagnosis and interpretation from medical images. We propose a new two-level alignment-based regularisation strategy that effectively leverages consistent intra-image information to enhance interpretation robustness and predictive performance.
  arXiv  Detail & Related papers  (2024-11-07T10:46:01Z)
• MMIL: A novel algorithm for disease associated cell type discovery [58.044870442206914]
  Single-cell datasets often lack individual cell labels, making it challenging to identify cells associated with disease. We introduce Mixture Modeling for Multiple Learning Instance (MMIL), an expectation method that enables the training and calibration of cell-level classifiers.
  arXiv  Detail & Related papers  (2024-06-12T15:22:56Z)
• A Clinical-oriented Multi-level Contrastive Learning Method for Disease Diagnosis in Low-quality Medical Images [4.576524795036682]
  Disease diagnosis methods guided by contrastive learning (CL) have shown significant advantages in lesion feature representation. We propose a clinical-oriented multi-level CL framework that aims to enhance the model's capacity to extract lesion features. The proposed CL framework is validated on two public medical image datasets, EyeQ and Chest X-ray.
  arXiv  Detail & Related papers  (2024-04-07T09:08:14Z)
• Iterative Online Image Synthesis via Diffusion Model for Imbalanced Classification [29.730360798234294]
  We introduce an Iterative Online Image Synthesis framework to address the class imbalance problem in medical image classification. Our framework incorporates two key modules, namely Online Image Synthesis (OIS) and Accuracy Adaptive Sampling (AAS) To evaluate the effectiveness of our proposed method in addressing imbalanced classification, we conduct experiments on the HAM10000 and APTOS datasets.
  arXiv  Detail & Related papers  (2024-03-13T10:51:18Z)
• STEM Rebalance: A Novel Approach for Tackling Imbalanced Datasets using SMOTE, Edited Nearest Neighbour, and Mixup [0.20482269513546458]
  Imbalanced datasets in medical imaging are characterized by skewed class proportions and scarcity of abnormal cases. This paper investigates the potential of using Mixup augmentation to generate new data points as a generic vicinal distribution. We focus on the breast cancer problem, where imbalanced datasets are prevalent.
  arXiv  Detail & Related papers  (2023-11-13T17:45:28Z)
• Pixel-Level Explanation of Multiple Instance Learning Models in Biomedical Single Cell Images [52.527733226555206]
  We investigate the use of four attribution methods to explain a multiple instance learning models. We study two datasets of acute myeloid leukemia with over 100 000 single cell images. We compare attribution maps with the annotations of a medical expert to see how the model's decision-making differs from the human standard.
  arXiv  Detail & Related papers  (2023-03-15T14:00:11Z)
• GraVIS: Grouping Augmented Views from Independent Sources for Dermatology Analysis [52.04899592688968]
  We propose GraVIS, which is specifically optimized for learning self-supervised features from dermatology images. GraVIS significantly outperforms its transfer learning and self-supervised learning counterparts in both lesion segmentation and disease classification tasks.
  arXiv  Detail & Related papers  (2023-01-11T11:38:37Z)
• Analyzing the Effects of Handling Data Imbalance on Learned Features from Medical Images by Looking Into the Models [50.537859423741644]
  Training a model on an imbalanced dataset can introduce unique challenges to the learning problem. We look deeper into the internal units of neural networks to observe how handling data imbalance affects the learned features.
  arXiv  Detail & Related papers  (2022-04-04T09:38:38Z)
• ACPL: Anti-curriculum Pseudo-labelling forSemi-supervised Medical Image Classification [22.5935068122522]
  We propose a new SSL algorithm, called anti-curriculum pseudo-labelling (ACPL) ACPL introduces novel techniques to select informative unlabelled samples, improving training balance and allowing the model to work for both multi-label and multi-class problems. Our method outperforms previous SOTA SSL methods on both datasets.
  arXiv  Detail & Related papers  (2021-11-25T05:31:52Z)
• G-MIND: An End-to-End Multimodal Imaging-Genetics Framework for Biomarker Identification and Disease Classification [49.53651166356737]
  We propose a novel deep neural network architecture to integrate imaging and genetics data, as guided by diagnosis, that provides interpretable biomarkers. We have evaluated our model on a population study of schizophrenia that includes two functional MRI (fMRI) paradigms and Single Nucleotide Polymorphism (SNP) data.
  arXiv  Detail & Related papers  (2021-01-27T19:28:04Z)
• Multi-label Thoracic Disease Image Classification with Cross-Attention Networks [65.37531731899837]
  We propose a novel scheme of Cross-Attention Networks (CAN) for automated thoracic disease classification from chest x-ray images. We also design a new loss function that beyond cross-entropy loss to help cross-attention process and is able to overcome the imbalance between classes and easy-dominated samples within each class.
  arXiv  Detail & Related papers  (2020-07-21T14:37:00Z)

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.