WEEP: A method for spatial interpretation of weakly supervised CNN models in computational pathology

• URL: http://arxiv.org/abs/2403.15238v2
• Date: Mon, 8 Apr 2024 16:14:45 GMT
• Title: WEEP: A method for spatial interpretation of weakly supervised CNN models in computational pathology
• Authors: Abhinav Sharma, Bojing Liu, Mattias Rantalainen,
• Abstract summary: We propose a novel method, Wsi rEgion sElection aPproach (WEEP), for model interpretation. We demonstrate WEEP on a binary classification task in the area of breast cancer computational pathology.
• Score: 0.36096289461554343
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Deep learning enables the modelling of high-resolution histopathology whole-slide images (WSI). Weakly supervised learning of tile-level data is typically applied for tasks where labels only exist on the patient or WSI level (e.g. patient outcomes or histological grading). In this context, there is a need for improved spatial interpretability of predictions from such models. We propose a novel method, Wsi rEgion sElection aPproach (WEEP), for model interpretation. It provides a principled yet straightforward way to establish the spatial area of WSI required for assigning a particular prediction label. We demonstrate WEEP on a binary classification task in the area of breast cancer computational pathology. WEEP is easy to implement, is directly connected to the model-based decision process, and offers information relevant to both research and diagnostic applications.

Related papers

• EP-SAM: Weakly Supervised Histopathology Segmentation via Enhanced Prompt with Segment Anything [3.760646312664378]
  Pathological diagnosis of diseases like cancer has conventionally relied on the evaluation of morphological features by physicians and pathologists. Recent advancements in compute-aided diagnosis (CAD) systems are gaining significant attention as diagnostic support tools. We present a weakly supervised semantic segmentation (WSSS) model by combining class activation map and Segment Anything Model (SAM)-based pseudo-labeling.
  arXiv  Detail & Related papers  (2024-10-17T14:55:09Z)
• Selecting Interpretability Techniques for Healthcare Machine Learning models [69.65384453064829]
  In healthcare there is a pursuit for employing interpretable algorithms to assist healthcare professionals in several decision scenarios. We overview a selection of eight algorithms, both post-hoc and model-based, that can be used for such purposes.
  arXiv  Detail & Related papers  (2024-06-14T17:49:04Z)
• Self-Contrastive Weakly Supervised Learning Framework for Prognostic Prediction Using Whole Slide Images [3.6330373579181927]
  Prognostic prediction poses a unique challenge as the ground truth labels are inherently weak. We propose a novel three-part framework comprising of a convolutional network based tissue segmentation algorithm for region of interest delineation. Our best models yield an AUC of 0.721 and 0.678 for recurrence and treatment outcome prediction respectively.
  arXiv  Detail & Related papers  (2024-05-24T06:45:36Z)
• An interpretable deep learning method for bearing fault diagnosis [12.069344716912843]
  We utilize a convolutional neural network (CNN) with Gradient-weighted Class Activation Mapping (Grad-CAM) visualizations to form an interpretable Deep Learning (DL) method for classifying bearing faults. During the model evaluation process, the proposed approach retrieves prediction basis samples from the health library according to the similarity of the feature importance.
  arXiv  Detail & Related papers  (2023-08-20T15:22:08Z)
• Generalizing Backpropagation for Gradient-Based Interpretability [103.2998254573497]
  We show that the gradient of a model is a special case of a more general formulation using semirings. This observation allows us to generalize the backpropagation algorithm to efficiently compute other interpretable statistics.
  arXiv  Detail & Related papers  (2023-07-06T15:19:53Z)
• Benchmarking Self-Supervised Learning on Diverse Pathology Datasets [10.868779327544688]
  Self-supervised learning has shown to be an effective method for utilizing unlabeled data. We execute the largest-scale study of SSL pre-training on pathology image data. For the first time, we apply SSL to the challenging task of nuclei instance segmentation.
  arXiv  Detail & Related papers  (2022-12-09T06:38:34Z)
• Spatial machine-learning model diagnostics: a model-agnostic distance-based approach [91.62936410696409]
  This contribution proposes spatial prediction error profiles (SPEPs) and spatial variable importance profiles (SVIPs) as novel model-agnostic assessment and interpretation tools. The SPEPs and SVIPs of geostatistical methods, linear models, random forest, and hybrid algorithms show striking differences and also relevant similarities. The novel diagnostic tools enrich the toolkit of spatial data science, and may improve ML model interpretation, selection, and design.
  arXiv  Detail & Related papers  (2021-11-13T01:50:36Z)
• A multi-stage machine learning model on diagnosis of esophageal manometry [50.591267188664666]
  The framework includes deep-learning models at the swallow-level stage and feature-based machine learning models at the study-level stage. This is the first artificial-intelligence-style model to automatically predict CC diagnosis of HRM study from raw multi-swallow data.
  arXiv  Detail & Related papers  (2021-06-25T20:09:23Z)
• FF-NSL: Feed-Forward Neural-Symbolic Learner [70.978007919101]
  This paper introduces a neural-symbolic learning framework, called Feed-Forward Neural-Symbolic Learner (FF-NSL) FF-NSL integrates state-of-the-art ILP systems based on the Answer Set semantics, with neural networks, in order to learn interpretable hypotheses from labelled unstructured data.
  arXiv  Detail & Related papers  (2021-06-24T15:38:34Z)
• Towards Interpretable Deep Learning Models for Knowledge Tracing [62.75876617721375]
  We propose to adopt the post-hoc method to tackle the interpretability issue for deep learning based knowledge tracing (DLKT) models. Specifically, we focus on applying the layer-wise relevance propagation (LRP) method to interpret RNN-based DLKT model. Experiment results show the feasibility using the LRP method for interpreting the DLKT model's predictions.
  arXiv  Detail & Related papers  (2020-05-13T04:03:21Z)
• Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images [4.001273534300757]
  computational pathology has the potential to enable objective diagnosis, therapeutic response prediction and identification of new morphological features of clinical relevance. Deep learning-based computational pathology approaches either require manual annotation of gigapixel whole slide images (WSIs) in fully-supervised settings or thousands of WSIs with slide-level labels in a weakly-supervised setting. Here we present CLAM - Clustering-constrained attention multiple instance learning.
  arXiv  Detail & Related papers  (2020-04-20T23:00:13Z)

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.