# Multi-Layer Pseudo-Supervision for Histopathology Tissue Semantic Segmentation using Patch-level Classification Labels

arXiv: https://arxiv.org/abs/2110.08048 · HTML: https://mlsanity.com/paper/2110.08048/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- Builds on 39 of its 75 references that are in the map.
- First posted on arXiv 14 Oct 2021.

## Builds on (most cited first, 30 of 39)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Relational inductive biases, deep learning, and graph networks](https://mlsanity.com/paper/1806.01261/) (2018)
- [Temporal Ensembling for Semi-Supervised Learning](https://mlsanity.com/paper/1610.02242/) (2016)
- [Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results](https://mlsanity.com/paper/1703.01780/) (2017)
- [HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images](https://mlsanity.com/paper/1812.06499/) (2018)
- [S4L: Self-Supervised Semi-Supervised Learning](https://mlsanity.com/paper/1905.03670/) (2019)
- [ScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1604.05144/) (2016)
- [Learning Pixel-level Semantic Affinity with Image-level Supervision for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/1803.10464/) (2018)
- [Self-supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2004.04581/) (2020)
- [What's the Point: Semantic Segmentation with Point Supervision](https://mlsanity.com/paper/1506.02106/) (2015)
- [BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1503.01640/) (2015)
- [Wider or Deeper: Revisiting the ResNet Model for Visual Recognition](https://mlsanity.com/paper/1611.10080/) (2016)
- [Deep neural network models for computational histopathology: A survey](https://mlsanity.com/paper/1912.12378/) (2019)
- [Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classification](https://mlsanity.com/paper/1504.07947/) (2015)
- [A Survey on Active Learning and Human-in-the-Loop Deep Learning for Medical Image Analysis](https://mlsanity.com/paper/1910.02923/) (2019)
- [Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation](https://mlsanity.com/paper/1706.04737/) (2017)
- [Weakly-Supervised Semantic Segmentation via Sub-category Exploration](https://mlsanity.com/paper/2008.01183/) (2020)
- [DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation](https://mlsanity.com/paper/1604.02677/) (2016)
- [Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation](https://mlsanity.com/paper/2006.16806/) (2020)
- [Exponential Moving Average Normalization for Self-supervised and Semi-supervised Learning](https://mlsanity.com/paper/2101.08482/) (2021)
- [Efficient Active Learning for Image Classification and Segmentation using a Sample Selection and Conditional Generative Adversarial Network](https://mlsanity.com/paper/1806.05473/) (2018)
- [Semi-supervised Medical Image Classification with Relation-driven Self-ensembling Model](https://mlsanity.com/paper/2005.07377/) (2020)
- [Weakly Supervised Deep Nuclei Segmentation Using Partial Points Annotation in Histopathology Images](https://mlsanity.com/paper/2007.05448/) (2020)
- [Scribble2Label: Scribble-Supervised Cell Segmentation via Self-Generating Pseudo-Labels with Consistency](https://mlsanity.com/paper/2006.12890/) (2020)
- [Constrained Deep Weak Supervision for Histopathology Image Segmentation](https://mlsanity.com/paper/1701.00794/) (2017)
- [Weakly supervised multiple instance learning histopathological tumor segmentation](https://mlsanity.com/paper/2004.05024/) (2020)
- [HookNet: multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide images](https://mlsanity.com/paper/2006.12230/) (2020)
- [Self-Loop Uncertainty: A Novel Pseudo-Label for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2007.09854/) (2020)

## Built on by (most cited first, 30 of 43)

- [CLIP in Medical Imaging: A Survey](https://mlsanity.com/paper/2312.07353/) (2023)
- [WSSS4LUAD: Grand Challenge on Weakly-supervised Tissue Semantic Segmentation for Lung Adenocarcinoma](https://mlsanity.com/paper/2204.06455/) (2022)
- [Prototype-Based Image Prompting for Weakly Supervised Histopathological Image Segmentation](https://mlsanity.com/paper/2503.12068/) (2025)
- [A Survey of Medical Vision-and-Language Applications and Their Techniques](https://mlsanity.com/paper/2411.12195/) (2024)
- [Weakly Supervised Joint Whole-Slide Segmentation and Classification in Prostate Cancer](https://mlsanity.com/paper/2301.02933/) (2023)
- [UniBiomed: A Universal Foundation Model for Grounded Biomedical Image Interpretation](https://mlsanity.com/paper/2504.21336/) (2025)
- [How Good is my Histopathology Vision-Language Foundation Model? A Holistic Benchmark](https://mlsanity.com/paper/2503.12990/) (2025)
- [A multi-modal vision-language model for generalizable annotation-free pathology localization](https://mlsanity.com/paper/2401.02044/) (2024)
- [WSI-SAM: Multi-resolution Segment Anything Model (SAM) for histopathology whole-slide images](https://mlsanity.com/paper/2403.09257/) (2024)
- [Benchmarking PathCLIP for Pathology Image Analysis](https://mlsanity.com/paper/2401.02651/) (2024)
- [SOTA: Self-adaptive Optimal Transport for Zero-Shot Classification with Multiple Foundation Models](https://mlsanity.com/paper/2506.13723/) (2025)
- [FedDBL: Communication and Data Efficient Federated Deep-Broad Learning for Histopathological Tissue Classification](https://mlsanity.com/paper/2302.12662/) (2023)
- [UM-CAM: Uncertainty-weighted Multi-resolution Class Activation Maps for Weakly-supervised Fetal Brain Segmentation](https://mlsanity.com/paper/2306.11490/) (2023)
- [Boundary-RL: Reinforcement Learning for Weakly-Supervised Prostate Segmentation in TRUS Images](https://mlsanity.com/paper/2308.11376/) (2023)
- [From Few to More: Scribble-based Medical Image Segmentation via Masked Context Modeling and Continuous Pseudo Labels](https://mlsanity.com/paper/2408.12814/) (2024)
- [PathoPainter: Augmenting Histopathology Segmentation via Tumor-aware Inpainting](https://mlsanity.com/paper/2503.04634/) (2025)
- [PathSegDiff: Pathology Segmentation using Diffusion model representations](https://mlsanity.com/paper/2504.06950/) (2025)
- [CVFC: Attention-Based Cross-View Feature Consistency for Weakly Supervised Semantic Segmentation of Pathology Images](https://mlsanity.com/paper/2308.10449/) (2023)
- [HisynSeg: Weakly-Supervised Histopathological Image Segmentation via Image-Mixing Synthesis and Consistency Regularization](https://mlsanity.com/paper/2412.20924/) (2024)
- [Superpixel Boundary Correction for Weakly-Supervised Semantic Segmentation on Histopathology Images](https://mlsanity.com/paper/2501.03891/) (2025)
- [Multimodal Prototype Alignment for Semi-supervised Pathology Image Segmentation](https://mlsanity.com/paper/2508.19574/) (2025)
- [LPD: Learnable Prototypes with Diversity Regularization for Weakly Supervised Histopathology Segmentation](https://mlsanity.com/paper/2512.05922/) (2025)
- [VISTA-PATH: An interactive foundation model for pathology image segmentation and quantitative analysis in computational pathology](https://mlsanity.com/paper/2601.16451/) (2026)
- [BroadCAM: Outcome-agnostic Class Activation Mapping for Small-scale Weakly Supervised Applications](https://mlsanity.com/paper/2309.03509/) (2023)
- [Enhancing Weakly-Supervised Histopathology Image Segmentation with Knowledge Distillation on MIL-Based Pseudo-Labels](https://mlsanity.com/paper/2407.10274/) (2024)
- [EP-SAM: Weakly Supervised Histopathology Segmentation via Enhanced Prompt with Segment Anything](https://mlsanity.com/paper/2410.13621/) (2024)
- [Towards Computation- and Communication-efficient Computational Pathology](https://mlsanity.com/paper/2504.02628/) (2025)
- [Weakly Supervised Teacher-Student Framework with Progressive Pseudo-mask Refinement for Gland Segmentation](https://mlsanity.com/paper/2603.08605/) (2026)
- [Preparing data for pathological artificial intelligence with clinical-grade performance](https://mlsanity.com/paper/2205.10748/) (2022)
- [Contrastive Centroid Supervision Alleviates Domain Shift in Medical Image Classification](https://mlsanity.com/paper/2205.15658/) (2022)

## Abstract (arXiv)

Tissue-level semantic segmentation is a vital step in computational pathology. Fully-supervised models have already achieved outstanding performance with dense pixel-level annotations. However, drawing such labels on the giga-pixel whole slide images is extremely expensive and time-consuming. In this paper, we use only patch-level classification labels to achieve tissue semantic segmentation on histopathology images, finally reducing the annotation efforts. We proposed a two-step model including a classification and a segmentation phases. In the classification phase, we proposed a CAM-based model to generate pseudo masks by patch-level labels. In the segmentation phase, we achieved tissue semantic segmentation by our proposed Multi-Layer Pseudo-Supervision. Several technical novelties have been proposed to reduce the information gap between pixel-level and patch-level annotations. As a part of this paper, we introduced a new weakly-supervised semantic segmentation (WSSS) dataset for lung adenocarcinoma (LUAD-HistoSeg). We conducted several experiments to evaluate our proposed model on two datasets. Our proposed model outperforms two state-of-the-art WSSS approaches. Note that we can achieve comparable quantitative and qualitative results with the fully-supervised model, with only around a 2% gap for MIoU and FwIoU. By comparing with manual labeling, our model can greatly save the annotation time from hours to minutes. The source code is available at: urlhttps://github.com/ChuHan89/WSSS-Tissue.