# Deep Interpretable Classification and Weakly-Supervised Segmentation of Histology Images via Max-Min Uncertainty

arXiv: https://arxiv.org/abs/2011.07221 · HTML: https://mlsanity.com/paper/2011.07221/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2020.

## Built on by (most cited first, 26 of 26)

- [Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysis](https://mlsanity.com/paper/2210.03736/) (2022)
- [A Review of Uncertainty Estimation and its Application in Medical Imaging](https://mlsanity.com/paper/2302.08119/) (2023)
- [A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methods](https://mlsanity.com/paper/2310.06873/) (2023)
- [Towards Label-efficient Automatic Diagnosis and Analysis: A Comprehensive Survey of Advanced Deep Learning-based Weakly-supervised, Semi-supervised and Self-supervised Techniques in Histopathological Image Analysis](https://mlsanity.com/paper/2208.08789/) (2022)
- [LE-UDA: Label-efficient unsupervised domain adaptation for medical image segmentation](https://mlsanity.com/paper/2212.02078/) (2022)
- [F-CAM: Full Resolution Class Activation Maps via Guided Parametric Upscaling](https://mlsanity.com/paper/2109.07069/) (2021)
- [Maximum Entropy on Erroneous Predictions (MEEP): Improving model calibration for medical image segmentation](https://mlsanity.com/paper/2112.12218/) (2021)
- [TCAM: Temporal Class Activation Maps for Object Localization in Weakly-Labeled Unconstrained Videos](https://mlsanity.com/paper/2208.14542/) (2022)
- [Guided Interpretable Facial Expression Recognition via Spatial Action Unit Cues](https://mlsanity.com/paper/2402.00281/) (2024)
- [SDCL: Students Discrepancy-Informed Correction Learning for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2409.16728/) (2024)
- [Negative Evidence Matters in Interpretable Histology Image Classification](https://mlsanity.com/paper/2201.02445/) (2022)
- [CoLo-CAM: Class Activation Mapping for Object Co-Localization in Weakly-Labeled Unconstrained Videos](https://mlsanity.com/paper/2303.09044/) (2023)
- [Self-supervised Multi-scale Consistency for Weakly Supervised Segmentation Learning](https://mlsanity.com/paper/2108.11900/) (2021)
- [Leveraging Transformers for Weakly Supervised Object Localization in Unconstrained Videos](https://mlsanity.com/paper/2407.06018/) (2024)
- [PixelCAM: Pixel Class Activation Mapping for Histology Image Classification and ROI Localization](https://mlsanity.com/paper/2503.24135/) (2025)
- [Challenging mitosis detection algorithms: Global labels allow centroid localization](https://mlsanity.com/paper/2211.16852/) (2022)
- [Advancing Histopathology with Deep Learning Under Data Scarcity: A Decade in Review](https://mlsanity.com/paper/2410.19820/) (2024)
- [RAIL: Region-Aware Instructive Learning for Semi-Supervised Tooth Segmentation in CBCT](https://mlsanity.com/paper/2505.03538/) (2025)
- [A Comprehensive Study on Medical Image Segmentation using Deep Neural Networks](https://mlsanity.com/paper/2506.04121/) (2025)
- [Bladder segmentation based on deep learning approaches: current limitations and lessons](https://mlsanity.com/paper/2101.06498/) (2021)
- [Leveraging Uncertainty for Deep Interpretable Classification and Weakly-Supervised Segmentation of Histology Images](https://mlsanity.com/paper/2205.05841/) (2022)
- [Attention-based Dynamic Subspace Learners for Medical Image Analysis](https://mlsanity.com/paper/2206.09068/) (2022)
- [Additive Class Distinction Maps using Branched-GANs](https://mlsanity.com/paper/2305.02899/) (2023)
- [Text-Driven Weakly Supervised OCT Lesion Segmentation with Structural Guidance](https://mlsanity.com/paper/2411.12615/) (2024)
- [ViCrop-Det: Spatial Attention Entropy Guided Cropping for Training-Free Small-Object Detection](https://mlsanity.com/paper/2604.26806/) (2026)
- [ZScribbleSeg: A comprehensive segmentation framework with modeling of efficient annotation and maximization of scribble supervision](https://mlsanity.com/paper/2605.06266/) (2026)

## Abstract (arXiv)

Weakly-supervised learning (WSL) has recently triggered substantial interest as it mitigates the lack of pixel-wise annotations. Given global image labels, WSL methods yield pixel-level predictions (segmentations), which enable to interpret class predictions. Despite their recent success, mostly with natural images, such methods can face important challenges when the foreground and background regions have similar visual cues, yielding high false-positive rates in segmentations, as is the case in challenging histology images. WSL training is commonly driven by standard classification losses, which implicitly maximize model confidence, and locate the discriminative regions linked to classification decisions. Therefore, they lack mechanisms for modeling explicitly non-discriminative regions and reducing false-positive rates. We propose novel regularization terms, which enable the model to seek both non-discriminative and discriminative regions, while discouraging unbalanced segmentations. We introduce high uncertainty as a criterion to localize non-discriminative regions that do not affect classifier decision, and describe it with original Kullback-Leibler (KL) divergence losses evaluating the deviation of posterior predictions from the uniform distribution. Our KL terms encourage high uncertainty of the model when the latter inputs the latent non-discriminative regions. Our loss integrates: (i) a cross-entropy seeking a foreground, where model confidence about class prediction is high; (ii) a KL regularizer seeking a background, where model uncertainty is high; and (iii) log-barrier terms discouraging unbalanced segmentations. Comprehensive experiments and ablation studies over the public GlaS colon cancer data and a Camelyon16 patch-based benchmark for breast cancer show substantial improvements over state-of-the-art WSL methods, and confirm the effect of our new regularizers.