# Multi-Instance Multi-Scale CNN for Medical Image Classification

arXiv: https://arxiv.org/abs/1907.02413 · HTML: https://mlsanity.com/paper/1907.02413/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jul 2019.

## Built on by (most cited first, 13 of 13)

- [DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classification](https://mlsanity.com/paper/2203.12081/) (2022)
- [Cluster-to-Conquer: A Framework for End-to-End Multi-Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2103.10626/) (2021)
- [DGMIL: Distribution Guided Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2206.08861/) (2022)
- [Semantic annotation for computational pathology: Multidisciplinary experience and best practice recommendations](https://mlsanity.com/paper/2106.13689/) (2021)
- [Interventional Multi-Instance Learning with Deconfounded Instance-Level Prediction](https://mlsanity.com/paper/2204.09204/) (2022)
- [Multi-scale Transformer Network with Edge-aware Pre-training for Cross-Modality MR Image Synthesis](https://mlsanity.com/paper/2212.01108/) (2022)
- [Stabilized Medical Image Attacks](https://mlsanity.com/paper/2103.05232/) (2021)
- [Anomaly-aware multiple instance learning for rare anemia disorder classification](https://mlsanity.com/paper/2207.01742/) (2022)
- [A Knowledge-based Learning Framework for Self-supervised Pre-training Towards Enhanced Recognition of Biomedical Microscopy Images](https://mlsanity.com/paper/2211.14715/) (2022)
- [Exploring Visual Prompts for Whole Slide Image Classification with Multiple Instance Learning](https://mlsanity.com/paper/2303.13122/) (2023)
- [SO(2) and O(2) Equivariance in Image Recognition with Bessel-Convolutional Neural Networks](https://mlsanity.com/paper/2304.09214/) (2023)
- [Distilling High Diagnostic Value Patches for Whole Slide Image Classification Using Attention Mechanism](https://mlsanity.com/paper/2407.19821/) (2024)
- [Manifold Learning for Hyperspectral Images](https://mlsanity.com/paper/2503.15016/) (2025)

## Abstract (arXiv)

Deep learning for medical image classification faces three major challenges: 1) the number of annotated medical images for training are usually small; 2) regions of interest (ROIs) are relatively small with unclear boundaries in the whole medical images, and may appear in arbitrary positions across the x,y (and also z in 3D images) dimensions. However often only labels of the whole images are annotated, and localized ROIs are unavailable; and 3) ROIs in medical images often appear in varying sizes (scales). We approach these three challenges with a Multi-Instance Multi-Scale (MIMS) CNN: 1) We propose a multi-scale convolutional layer, which extracts patterns of different receptive fields with a shared set of convolutional kernels, so that scale-invariant patterns are captured by this compact set of kernels. As this layer contains only a small number of parameters, training on small datasets becomes feasible; 2) We propose a "top-k pooling" to aggregate the feature maps in varying scales from multiple spatial dimensions, allowing the model to be trained using weak annotations within the multiple instance learning (MIL) framework. Our method is shown to perform well on three classification tasks involving two 3D and two 2D medical image datasets.