# Multitask Classification and Segmentation for Cancer Diagnosis in Mammography

arXiv: https://arxiv.org/abs/1909.05397 · HTML: https://mlsanity.com/paper/1909.05397/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2019.

## Built on by (most cited first, 9 of 9)

- [U-GAT: Multimodal Graph Attention Network for COVID-19 Outcome Prediction](https://mlsanity.com/paper/2108.00860/) (2021)
- [Generalizable multi-task, multi-domain deep segmentation of sparse pediatric imaging datasets via multi-scale contrastive regularization and multi-joint anatomical priors](https://mlsanity.com/paper/2207.13502/) (2022)
- [Evaluation of Complexity Measures for Deep Learning Generalization in Medical Image Analysis](https://mlsanity.com/paper/2103.03328/) (2021)
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- [Segmentation-Consistent Probabilistic Lesion Counting](https://mlsanity.com/paper/2204.05276/) (2022)
- [Ultrasound Detection of Subquadricipital Recess Distension](https://mlsanity.com/paper/2211.12089/) (2022)
- [Ammonia-Net: A Multi-task Joint Learning Model for Multi-class Segmentation and Classification in Tooth-marked Tongue Diagnosis](https://mlsanity.com/paper/2310.03472/) (2023)
- [Multi-Task Multi-Scale Learning For Outcome Prediction in 3D PET Images](https://mlsanity.com/paper/2203.00641/) (2022)
- [Design Perspectives of Multitask Deep Learning Models and Applications](https://mlsanity.com/paper/2209.13444/) (2022)

## Abstract (arXiv)

Annotation cost is a bottleneck for collecting massive data in mammography, especially for training deep neural networks. In this paper, we study the use of heterogeneous levels of annotation granularity to improve predictive performances. More precisely, we introduce a multi-task learning scheme for training convolutional neural network (ConvNets), which combines segmentation and classification, using image-level and pixel-level annotations. In this way, different objectives can be used to regularize training by sharing intermediate deep representations. Successful experiments are carried out on the Digital Database of Screening Mammography (DDSM) to validate the relevance of the proposed approach.