Constrained Deep Weak Supervision for Histopathology Image Segmentation (1701.00794v1)

Abstract: In this paper, we develop a new weakly-supervised learning algorithm to learn to segment cancerous regions in histopathology images. Our work is under a multiple instance learning framework (MIL) with a new formulation, deep weak supervision (DWS); we also propose an effective way to introduce constraints to our neural networks to assist the learning process. The contributions of our algorithm are threefold: (1) We build an end-to-end learning system that segments cancerous regions with fully convolutional networks (FCN) in which image-to-image weakly-supervised learning is performed. (2) We develop a deep week supervision formulation to exploit multi-scale learning under weak supervision within fully convolutional networks. (3) Constraints about positive instances are introduced in our approach to effectively explore additional weakly-supervised information that is easy to obtain and enjoys a significant boost to the learning process. The proposed algorithm, abbreviated as DWS-MIL, is easy to implement and can be trained efficiently. Our system demonstrates state-of-the-art results on large-scale histopathology image datasets and can be applied to various applications in medical imaging beyond histopathology images such as MRI, CT, and ultrasound images.

• The paper presents an end-to-end FCN-based framework for weakly supervised segmentation of cancerous regions.
• It employs deep weak supervision with multi-scale side outputs to capture intricate histopathological features.
• Area constraints integrated into the model significantly enhance segmentation accuracy over traditional MIL methods.

Constrained Deep Weak Supervision for Histopathology Image Segmentation

The discussed paper presents a novel approach to histopathology image segmentation through a constrained deep weak supervision framework, termed DWS-MIL (Deep Weak Supervision for Multiple Instance Learning). The authors introduce a system that leverages the potential of fully convolutional networks (FCNs) to perform image-to-image segmentation under weak supervision, focusing on detecting cancerous regions in large-scale histopathology images. This approach is particularly noteworthy as it operates under a multiple instance learning (MIL) paradigm, which balances the need for detailed instance-level information while only requiring image-level annotations.

The core contributions of the paper are multi-fold:

1. End-to-End Learning System: The framework developed is capable of segmenting cancerous regions using an end-to-end learning system by employing fully convolutional networks. This system is trained in a weakly supervised manner, using only image-level labels to infer detailed pixel-level predictions.
2. Deep Weak Supervision (DWS): This innovative formulation enables multi-scale learning through side-output layers connected to different stages of the FCN. These layers act as intermediate supervision signals, which refine the learning process by incorporating information from multiple scales. This deep supervision technique allows the learning model to effectively capture and process nested multi-scale features inherent in medical images.
3. Incorporation of Area Constraints: The authors further enhance the learning framework by integrating easy-to-obtain area estimates as additional constraints. These area constraints serve to inform the model about the expected size of the cancerous regions, thereby improving segmentation accuracy. This element of supervision is particularly efficient as it requires only a small degree of additional annotation effort but significantly boosts segmentation performance.

Numerical Performance and Results

The numerical results presented in the paper are compelling. The proposed DWS-MIL and its constrained variant CDWS-MIL achieve remarkable segmentation accuracy on histopathology datasets consisting of colon cancer images. When compared to traditional and patch-based approaches like MIL-Boosting, the CDWS-MIL substantially outperforms them, achieving higher F-measure scores on both cancerous and non-cancerous images. Specifically, the inclusion of area constraints provides a notable improvement over the baseline method, evidencing the utility of this additional weak super-vision.

Theoretical and Practical Implications

The theoretical significance of this work lies in its novel approach of integrating hierarchical supervision signals in a weakly-supervised learning framework. By structuring the FCN to accommodate deep weak supervision, the paper extends the MIL framework to support sophisticated segmentation tasks without the need for dense pixel-level annotations. This progression opens the possibility of applying similar methods to other domains within medical imaging and beyond, such as MRI and CT scans, where fully annotated datasets are often scarce or labor-intensive to obtain.

Practically, the implications are vast. The methodology offers a scalable and efficient approach for automating the segmentation of histopathology images, which can potentially accelerate diagnosis processes in clinical settings. This approach might reduce the workload on pathologists by efficiently pre-segmenting potential cancerous regions, thus allowing experts to focus their expertise on high-probability areas.

Future Directions

The research suggests several interesting directions for future work. One potential development is exploring how the deep weak supervision framework can be adapted to handle multi-class segmentation tasks, which would be highly valuable in classifying different types of tissues beyond binary cancer detection. Additionally, the impact of integrating more sophisticated constraint types, possibly derived from other medical imaging modalities, could further refine predictive capabilities. Expanding this model's application to real-time clinical settings, while navigating challenges related to computational costs and robustness across diverse pathology slides, represents an exciting avenue for continued exploration.

In essence, the paper delineates a sophisticated strategy for tackling the significant challenge of histopathology image segmentation under minimal supervision, setting the stage for future advancements in medical imaging analysis.