# Y-Net: Joint Segmentation and Classification for Diagnosis of Breast Biopsy Images

arXiv: https://arxiv.org/abs/1806.01313 · HTML: https://mlsanity.com/paper/1806.01313/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jun 2018.

## Built on by (most cited first, 30 of 42)

- [Medical Transformer: Gated Axial-Attention for Medical Image Segmentation](https://mlsanity.com/paper/2102.10662/) (2021)
- [UNeXt: MLP-based Rapid Medical Image Segmentation Network](https://mlsanity.com/paper/2203.04967/) (2022)
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- [A Survey on Graph-Based Deep Learning for Computational Histopathology](https://mlsanity.com/paper/2107.00272/) (2021)
- [CLIP in Medical Imaging: A Survey](https://mlsanity.com/paper/2312.07353/) (2023)
- [Q-Net: Query-Informed Few-Shot Medical Image Segmentation](https://mlsanity.com/paper/2208.11451/) (2022)
- [Pyramid Medical Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2104.14702/) (2021)
- [BSDA-Net: A Boundary Shape and Distance Aware Joint Learning Framework for Segmenting and Classifying OCTA Images](https://mlsanity.com/paper/2107.04823/) (2021)
- [FetalNet: Multi-task Deep Learning Framework for Fetal Ultrasound Biometric Measurements](https://mlsanity.com/paper/2107.06943/) (2021)
- [Vessel-CAPTCHA: an efficient learning framework for vessel annotation and segmentation](https://mlsanity.com/paper/2101.09321/) (2021)
- [MCUa: Multi-level Context and Uncertainty aware Dynamic Deep Ensemble for Breast Cancer Histology Image Classification](https://mlsanity.com/paper/2108.10709/) (2021)
- [DeepMTS: Deep Multi-task Learning for Survival Prediction in Patients with Advanced Nasopharyngeal Carcinoma using Pretreatment PET/CT](https://mlsanity.com/paper/2109.07711/) (2021)
- [Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions](https://mlsanity.com/paper/2304.06662/) (2023)
- [MT-TransUNet: Mediating Multi-Task Tokens in Transformers for Skin Lesion Segmentation and Classification](https://mlsanity.com/paper/2112.01767/) (2021)
- [Uncertainty-informed Mutual Learning for Joint Medical Image Classification and Segmentation](https://mlsanity.com/paper/2303.10049/) (2023)
- [U-GAT: Multimodal Graph Attention Network for COVID-19 Outcome Prediction](https://mlsanity.com/paper/2108.00860/) (2021)
- [Generalized Multi-Task Learning from Substantially Unlabeled Multi-Source Medical Image Data](https://mlsanity.com/paper/2110.13185/) (2021)
- [Unified Supervision For Vision-Language Modeling in 3D Computed Tomography](https://mlsanity.com/paper/2509.01554/) (2025)
- [Deep Learning Fetal Ultrasound Video Model Match Human Observers in Biometric Measurements](https://mlsanity.com/paper/2205.13835/) (2022)
- [Hybrid Skip: A Biologically Inspired Skip Connection for the UNet Architecture](https://mlsanity.com/paper/2207.04721/) (2022)
- [Valuing Vicinity: Memory attention framework for context-based semantic segmentation in histopathology](https://mlsanity.com/paper/2210.11822/) (2022)
- [DRG-Net: Interactive Joint Learning of Multi-lesion Segmentation and Classification for Diabetic Retinopathy Grading](https://mlsanity.com/paper/2212.14615/) (2022)
- [HALOS: Hallucination-free Organ Segmentation after Organ Resection Surgery](https://mlsanity.com/paper/2303.07717/) (2023)
- [Boosting Convolution with Efficient MLP-Permutation for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/2303.13111/) (2023)
- [CM-MLP: Cascade Multi-scale MLP with Axial Context Relation Encoder for Edge Segmentation of Medical Image](https://mlsanity.com/paper/2208.10701/) (2022)
- [FAS-UNet: A Novel FAS-driven Unet to Learn Variational Image Segmentation](https://mlsanity.com/paper/2210.15164/) (2022)
- [Enhancing Medical Image Segmentation: Optimizing Cross-Entropy Weights and Post-Processing with Autoencoders](https://mlsanity.com/paper/2308.10488/) (2023)
- [Multi-task UNet architecture for end-to-end autonomous driving](https://mlsanity.com/paper/2112.08967/) (2021)
- [Tips and Tricks to Improve CNN-based Chest X-ray Diagnosis: A Survey](https://mlsanity.com/paper/2106.00997/) (2021)
- [Reconstructing Images of Two Adjacent Objects through Scattering Medium Using Generative Adversarial Network](https://mlsanity.com/paper/2107.11574/) (2021)

## Abstract (arXiv)

In this paper, we introduce a conceptually simple network for generating discriminative tissue-level segmentation masks for the purpose of breast cancer diagnosis. Our method efficiently segments different types of tissues in breast biopsy images while simultaneously predicting a discriminative map for identifying important areas in an image. Our network, Y-Net, extends and generalizes U-Net by adding a parallel branch for discriminative map generation and by supporting convolutional block modularity, which allows the user to adjust network efficiency without altering the network topology. Y-Net delivers state-of-the-art segmentation accuracy while learning 6.6x fewer parameters than its closest competitors. The addition of descriptive power from Y-Net's discriminative segmentation masks improve diagnostic classification accuracy by 7% over state-of-the-art methods for diagnostic classification. Source code is available at: https://sacmehta.github.io/YNet.