ProtoMIL: Multiple Instance Learning with Prototypical Parts for Fine-Grained Interpretability

• URL: http://arxiv.org/abs/2108.10612v1
• Date: Tue, 24 Aug 2021 10:02:31 GMT
• Title: ProtoMIL: Multiple Instance Learning with Prototypical Parts for Fine-Grained Interpretability
• Authors: Dawid Rymarczyk and Aneta Kaczy\'nska and Jaros{\l}aw Kraus and Adam Pardyl and Bartosz Zieli\'nski
• Abstract summary: Multiple Instance Learning (MIL) gains popularity in many real-life machine learning applications due to its weakly supervised nature. In this paper, we introduce ProtoMIL, a novel self-explainable MIL method inspired by the case-based reasoning process that operates on visual prototypes. Thanks to incorporating prototypical features into objects description, ProtoMIL unprecedentedly joins the model accuracy and fine-grained interpretability.
• Score: 2.094672430475796
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multiple Instance Learning (MIL) gains popularity in many real-life machine learning applications due to its weakly supervised nature. However, the corresponding effort on explaining MIL lags behind, and it is usually limited to presenting instances of a bag that are crucial for a particular prediction. In this paper, we fill this gap by introducing ProtoMIL, a novel self-explainable MIL method inspired by the case-based reasoning process that operates on visual prototypes. Thanks to incorporating prototypical features into objects description, ProtoMIL unprecedentedly joins the model accuracy and fine-grained interpretability, which we present with the experiments on five recognized MIL datasets.

Related papers

• xMIL: Insightful Explanations for Multiple Instance Learning in Histopathology [13.939494815120666]
  Multiple instance learning (MIL) is an effective and widely used approach for weakly supervised machine learning. We revisit MIL through the lens of explainable AI (XAI) and introduce xMIL, a refined framework with more general assumptions. Our approach consistently outperforms previous explanation attempts with particularly improved faithfulness scores on challenging biomarker prediction tasks.
  arXiv  Detail & Related papers  (2024-06-06T17:26:40Z)
• Hyperbolic Secant representation of the logistic function: Application to probabilistic Multiple Instance Learning for CT intracranial hemorrhage detection [0.0]
  Multiple Instance Learning (MIL) is a weakly supervised paradigm that has been successfully applied to many different scientific areas. We propose a general GP-based MIL method that takes different forms by simply leveraging distributions other than the Hyperbolic Secant one. This is validated in a comprehensive experimental study including one synthetic MIL dataset, two well-known MIL benchmarks, and a real-world medical problem.
  arXiv  Detail & Related papers  (2024-03-21T20:43:34Z)
• Reproducibility in Multiple Instance Learning: A Case For Algorithmic Unit Tests [59.623267208433255]
  Multiple Instance Learning (MIL) is a sub-domain of classification problems with positive and negative labels and a "bag" of inputs. In this work, we examine five of the most prominent deep-MIL models and find that none of them respects the standard MIL assumption. We identify and demonstrate this problem via a proposed "algorithmic unit test", where we create synthetic datasets that can be solved by a MIL respecting model.
  arXiv  Detail & Related papers  (2023-10-27T03:05:11Z)
• PDL: Regularizing Multiple Instance Learning with Progressive Dropout Layers [2.069061136213899]
  Multiple instance learning (MIL) was a weakly supervised learning approach that sought to assign binary class labels to collections of instances known as bags. We present a novel approach in the form of a Progressive Dropout Layer (PDL) to address overfitting and empower the MIL model in uncovering intricate and impactful feature representations.
  arXiv  Detail & Related papers  (2023-08-19T21:20:30Z)
• MAML is a Noisy Contrastive Learner [72.04430033118426]
  Model-agnostic meta-learning (MAML) is one of the most popular and widely-adopted meta-learning algorithms nowadays. We provide a new perspective to the working mechanism of MAML and discover that: MAML is analogous to a meta-learner using a supervised contrastive objective function. We propose a simple but effective technique, zeroing trick, to alleviate such interference.
  arXiv  Detail & Related papers  (2021-06-29T12:52:26Z)
• CIL: Contrastive Instance Learning Framework for Distantly Supervised Relation Extraction [52.94486705393062]
  We go beyond typical multi-instance learning (MIL) framework and propose a novel contrastive instance learning (CIL) framework. Specifically, we regard the initial MIL as the relational triple encoder and constraint positive pairs against negative pairs for each instance. Experiments demonstrate the effectiveness of our proposed framework, with significant improvements over the previous methods on NYT10, GDS and KBP.
  arXiv  Detail & Related papers  (2021-06-21T04:51:59Z)
• Masked Language Modeling and the Distributional Hypothesis: Order Word Matters Pre-training for Little [74.49773960145681]
  A possible explanation for the impressive performance of masked language model (MLM)-training is that such models have learned to represent the syntactic structures prevalent in NLP pipelines. In this paper, we propose a different explanation: pre-trains succeed on downstream tasks almost entirely due to their ability to model higher-order word co-occurrence statistics. Our results show that purely distributional information largely explains the success of pre-training, and underscore the importance of curating challenging evaluation datasets that require deeper linguistic knowledge.
  arXiv  Detail & Related papers  (2021-04-14T06:30:36Z)
• Dual-stream Maximum Self-attention Multi-instance Learning [11.685285490589981]
  Multi-instance learning (MIL) is a form of weakly supervised learning where a single class label is assigned to a bag of instances while the instance-level labels are not available. We propose a dual-stream maximum self-attention MIL model (DSMIL) parameterized by neural networks. Our method achieves superior performance compared to the best MIL methods and demonstrates state-of-the-art performance on benchmark MIL datasets.
  arXiv  Detail & Related papers  (2020-06-09T22:44:58Z)
• Prototypical Contrastive Learning of Unsupervised Representations [171.3046900127166]
  Prototypical Contrastive Learning (PCL) is an unsupervised representation learning method. PCL implicitly encodes semantic structures of the data into the learned embedding space. PCL outperforms state-of-the-art instance-wise contrastive learning methods on multiple benchmarks.
  arXiv  Detail & Related papers  (2020-05-11T09:53:36Z)
• Exploit Clues from Views: Self-Supervised and Regularized Learning for Multiview Object Recognition [66.87417785210772]
  This work investigates the problem of multiview self-supervised learning (MV-SSL) A novel surrogate task for self-supervised learning is proposed by pursuing "object invariant" representation. Experiments shows that the recognition and retrieval results using view invariant prototype embedding (VISPE) outperform other self-supervised learning methods.
  arXiv  Detail & Related papers  (2020-03-28T07:06:06Z)

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.