Y-Net: Joint Segmentation and Classification for Diagnosis of Breast Biopsy Images (1806.01313v1)

Abstract: 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.

• The paper presents Y-Net as a novel framework integrating segmentation and classification, reducing learned parameters while boosting diagnostic accuracy.
• It employs a dual-output mechanism with modular blocks to generate discriminative maps and precise segmentation masks of biopsy images.
• Experimental results demonstrate a 7% improvement in classification accuracy and state-of-the-art segmentation performance over traditional methods.

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

Y-Net introduces a novel deep learning framework aimed at enhancing the automated diagnosis of breast cancer from biopsy images. This system synergistically combines segmentation and classification tasks to improve diagnostic accuracy while maintaining network efficiency. Breast cancer diagnosis relies heavily on the accurate interpretation of biopsy images, a task where errors could lead to detrimental treatment outcomes. The paper addresses this critical challenge by proposing an innovative approach through the development of Y-Net, extending the well-established U-Net architecture with additional features tailored to segmentation and classification of biomedical images.

Key Contributions

Y-Net advances U-Net by incorporating a parallel branch to generate discriminative maps alongside segmentation masks. This dual-output mechanism provides nuanced insights into the tissue structure and assists in highlighting diagnostically significant regions within breast biopsy images. Notably, Y-Net facilitates a modular block setup, allowing flexibility in network configuration for various tasks without necessitating changes in topology, thereby aiding efficient model adaptability across different instances.

Methodology and Results

The research leverages convolutional neural networks (CNNs), emphasizing their proficiency in both segmentation and classification tasks in the medical imaging domain. Y-Net efficiently handles large Whole Slide Images (WSIs), overcoming traditional limitations in direct WSI processing by adopting sliding-window approaches.

The experimental results demonstrate Y-Net's success in reducing parameter complexity, achieving $6.6\times$ fewer learned parameters compared to competing methods, while delivering state-of-the-art segmentation performance. Moreover, the discriminative segmentation masks produced by Y-Net facilitated a notable improvement in diagnostic classification accuracy by 7% over existing techniques. The paper provides an extensive ablation paper to underscore the network's efficiency gains and validate performance improvements across varied network configurations.

Implications and Future Work

The Y-Net framework introduces significant advancements in the automated processing of medical images, particularly in breast cancer diagnosis. The proposed modular architecture is a pivotal feature, offering scalability and efficiency in model exploration. These findings could serve as a foundation for further research, fostering the development of adaptable network structures tailored for diverse imaging challenges in the medical field.

Additionally, the ability of Y-Net to efficaciously combine segmentation and classification tasks lays the groundwork for future exploration into multi-task learning in medical diagnostics. The framework could be expanded to incorporate additional imaging modalities or adapted for other pathological conditions, thereby progressing towards universal diagnostic solutions in healthcare.

Conclusion

The paper presents a compelling case for the integration of modular and efficient deep learning architectures in breast cancer diagnosis, highlighting Y-Net as a robust solution that bridges segmentation and classification. The demonstrated improvements in both parameter efficiency and diagnostic accuracy exemplify the potential of such frameworks to revolutionize medical imaging applications. As the role of AI in healthcare continues to grow, Y-Net's approach may inspire continued innovation and implementation of deep learning across varied diagnostic arenas.