# Adaptive Weighting Multi-Field-of-View CNN for Semantic Segmentation in Pathology

arXiv: https://arxiv.org/abs/1904.06040 · HTML: https://mlsanity.com/paper/1904.06040/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Apr 2019.

## Built on by (most cited first, 30 of 36)

- [Dynamic Neural Networks: A Survey](https://mlsanity.com/paper/2102.04906/) (2021)
- [Visual Language Pretrained Multiple Instance Zero-Shot Transfer for Histopathology Images](https://mlsanity.com/paper/2306.07831/) (2023)
- [Dynamic Multimodal Fusion](https://mlsanity.com/paper/2204.00102/) (2022)
- [Differentiable Zooming for Multiple Instance Learning on Whole-Slide Images](https://mlsanity.com/paper/2204.12454/) (2022)
- [Progressive Semantic Segmentation](https://mlsanity.com/paper/2104.03778/) (2021)
- [All you need are a few pixels: semantic segmentation with PixelPick](https://mlsanity.com/paper/2104.06394/) (2021)
- [OCELOT: Overlapped Cell on Tissue Dataset for Histopathology](https://mlsanity.com/paper/2303.13110/) (2023)
- [Diagnose Like a Pathologist: Transformer-Enabled Hierarchical Attention-Guided Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2301.08125/) (2023)
- [ScoreNet: Learning Non-Uniform Attention and Augmentation for Transformer-Based Histopathological Image Classification](https://mlsanity.com/paper/2202.07570/) (2022)
- [Cross-scale Multi-instance Learning for Pathological Image Diagnosis](https://mlsanity.com/paper/2304.00216/) (2023)
- [DPA-P2PNet: Deformable Proposal-aware P2PNet for Accurate Point-based Cell Detection](https://mlsanity.com/paper/2303.02602/) (2023)
- [DSNet: A Dual-Stream Framework for Weakly-Supervised Gigapixel Pathology Image Analysis](https://mlsanity.com/paper/2109.05788/) (2021)
- [Graph Flow: Cross-layer Graph Flow Distillation for Dual Efficient Medical Image Segmentation](https://mlsanity.com/paper/2203.08667/) (2022)
- [Enhanced Self-Checkout System for Retail Based on Improved YOLOv10](https://mlsanity.com/paper/2407.21308/) (2024)
- [Multi-Resolution Pathology-Language Pre-training Model with Text-Guided Visual Representation](https://mlsanity.com/paper/2504.18856/) (2025)
- [CoCo DistillNet: a Cross-layer Correlation Distillation Network for Pathological Gastric Cancer Segmentation](https://mlsanity.com/paper/2108.12173/) (2021)
- [Towards Effective and Efficient Context-aware Nucleus Detection in Histopathology Whole Slide Images](https://mlsanity.com/paper/2503.05678/) (2025)
- [Cross-scale Attention Guided Multi-instance Learning for Crohn's Disease Diagnosis with Pathological Images](https://mlsanity.com/paper/2208.07322/) (2022)
- [Case-based Similar Image Retrieval for Weakly Annotated Large Histopathological Images of Malignant Lymphoma Using Deep Metric Learning](https://mlsanity.com/paper/2107.03602/) (2021)
- [Valuing Vicinity: Memory attention framework for context-based semantic segmentation in histopathology](https://mlsanity.com/paper/2210.11822/) (2022)
- [CD-Net: Histopathology Representation Learning using Pyramidal Context-Detail Network](https://mlsanity.com/paper/2203.15078/) (2022)
- [Deep Multi-Scale U-Net Architecture and Label-Noise Robust Training Strategies for Histopathological Image Segmentation](https://mlsanity.com/paper/2205.01777/) (2022)
- [Transformer-based Personalized Attention Mechanism for Medical Images with Clinical Records](https://mlsanity.com/paper/2206.03003/) (2022)
- [Magnifying Networks for Images with Billions of Pixels](https://mlsanity.com/paper/2112.06121/) (2021)
- [COVID-19 Infection Segmentation from Chest CT Images Based on Scale Uncertainty](https://mlsanity.com/paper/2201.03053/) (2022)
- [Language models are good pathologists: using attention-based sequence reduction and text-pretrained transformers for efficient WSI classification](https://mlsanity.com/paper/2211.07384/) (2022)
- [Domain Generalization of Pathological Image Segmentation by Patch-Level and WSI-Level Contrastive Learning](https://mlsanity.com/paper/2508.07539/) (2025)
- [WSI-INR: Implicit Neural Representations for Lesion Segmentation in Whole-Slide Images](https://mlsanity.com/paper/2603.03749/) (2026)
- [Patch-Based Cervical Cancer Segmentation using Distance from Boundary of Tissue](https://mlsanity.com/paper/2108.08508/) (2021)
- [Data and Knowledge Co-driving for Cancer Subtype Classification on Multi-Scale Histopathological Slides](https://mlsanity.com/paper/2304.09314/) (2023)

## Abstract (arXiv)

Automated digital histopathology image segmentation is an important task to help pathologists diagnose tumors and cancer subtypes. For pathological diagnosis of cancer subtypes, pathologists usually change the magnification of whole-slide images (WSI) viewers. A key assumption is that the importance of the magnifications depends on the characteristics of the input image, such as cancer subtypes. In this paper, we propose a novel semantic segmentation method, called Adaptive-Weighting-Multi-Field-of-View-CNN (AWMF-CNN), that can adaptively use image features from images with different magnifications to segment multiple cancer subtype regions in the input image. The proposed method aggregates several expert CNNs for images of different magnifications by adaptively changing the weight of each expert depending on the input image. It leverages information in the images with different magnifications that might be useful for identifying the subtypes. It outperformed other state-of-the-art methods in experiments.