# Terabyte-scale Deep Multiple Instance Learning for Classification and Localization in Pathology

arXiv: https://arxiv.org/abs/1805.06983 · HTML: https://mlsanity.com/paper/1805.06983/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 May 2018.

## Built on by (most cited first, 16 of 16)

- [A Comprehensive Review of Computer-aided Whole-slide Image Analysis: from Datasets to Feature Extraction, Segmentation, Classification, and Detection Approaches](https://mlsanity.com/paper/2102.10553/) (2021)
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- [DiagSet: a dataset for prostate cancer histopathological image classification](https://mlsanity.com/paper/2105.04014/) (2021)
- [Benchmarking Embedding Aggregation Methods in Computational Pathology: A Clinical Data Perspective](https://mlsanity.com/paper/2407.07841/) (2024)
- [A State-of-the-art Survey of Artificial Neural Networks for Whole-slide Image Analysis:from Popular Convolutional Neural Networks to Potential Visual Transformers](https://mlsanity.com/paper/2104.06243/) (2021)
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- [Multimodal Generalized Zero Shot Learning for Gleason Grading using Self-Supervised Learning](https://mlsanity.com/paper/2111.07646/) (2021)
- [The impact of tissue detection on diagnostic artificial intelligence algorithms in digital pathology](https://mlsanity.com/paper/2503.23021/) (2025)
- [Weakly supervised pan-cancer segmentation tool](https://mlsanity.com/paper/2105.04269/) (2021)
- [Interpretable Saliency Maps And Self-Supervised Learning For Generalized Zero Shot Medical Image Classification](https://mlsanity.com/paper/2204.01728/) (2022)
- [Magnifying Networks for Images with Billions of Pixels](https://mlsanity.com/paper/2112.06121/) (2021)
- [Weakly-supervised learning for image-based classification of primary melanomas into genomic immune subgroups](https://mlsanity.com/paper/2202.11524/) (2022)
- [mTREE: Multi-Level Text-Guided Representation End-to-End Learning for Whole Slide Image Analysis](https://mlsanity.com/paper/2405.17824/) (2024)
- [Interpretable and Interactive Deep Multiple Instance Learning for Dental Caries Classification in Bitewing X-rays](https://mlsanity.com/paper/2112.09694/) (2021)
- [Computational Pathology for Brain Disorders](https://mlsanity.com/paper/2301.07030/) (2023)
- [Classifying Whole Slide Images: What Matters?](https://mlsanity.com/paper/2310.03279/) (2023)

## Abstract (arXiv)

In the field of computational pathology, the use of decision support systems powered by state-of-the-art deep learning solutions has been hampered by the lack of large labeled datasets. Until recently, studies relied on datasets in the order of few hundreds of slides which are not enough to train a model that can work at scale in the clinic. Here, we have gathered a dataset consisting of 12,160 slides, two orders of magnitude larger than previous datasets in pathology and equivalent to 25 times the pixel count of the entire ImageNet dataset. Given the size of our dataset it is possible for us to train a deep learning model under the Multiple Instance Learning (MIL) assumption where only the overall slide diagnosis is necessary for training, avoiding all the expensive pixel-wise annotations that are usually part of supervised learning approaches. We test our framework on a complex task, that of prostate cancer diagnosis on needle biopsies. We performed a thorough evaluation of the performance of our MIL pipeline under several conditions achieving an AUC of 0.98 on a held-out test set of 1,824 slides. These results open the way for training accurate diagnosis prediction models at scale, laying the foundation for decision support system deployment in the clinic.