Paper Summary

The paper proposes using Generative Adversarial Networks (GANs) to augment the dataset with high quality synthetic liver lesion images in order to improve the CNN classification performance for medical image classification. The authors use limited dataset of computed tomography (CT) images of 182 liver lesions (53 cysts, 64 metastases and 65 hemangiomas). The liver lesions vary considerably in shape, contrast and size, and also present intra-class variability. ...

In this paper, the authors proposed a fully convolutional neural network architecture for biomedical image segmentation which overcame the limitations of the contemporary algorithms. Unlike other popular algorithms then, the proposed network did not suffer from the redundancy arising out of overlapping training patches. Moreover, the authors eliminate the trade-off between localization accuracy and the use of context and state that “good localization and the use of context are possible at the same time”. ...

As the title suggests, this paper studies various different deep convolutional neural network architectures and various techniques to use these CNNs for CADe (Computer Aided Detection) tasks. With the tremendous popularity of CNN models, the authors state that the “tremendous” success of CNNs in medical image tasks has been primarily using three techniques. ...

Drawing inspiration from the popular VGG networks, the paper proposes using a deep convolutional neural network architecture with small convolutional kernels for segmentation of gliomas in MRI images. The authors discuss the relative advantages of using small kernels, and also explore the use of intensity normalization as a pre-processing step, which was unconventional in CNN-based segmentation methods. The proposed algorithm obtained the first position for the complete, the core, and the enhancing regions in Dice Similarity Coefficient metric in the Brain Tumor Segmentation Challenge 2013 database (BraTS 2013). ...

The authors propose to reformulate the problem of anatomical detection as a cognitive learning task for an artificial agent. Given a volumetric image $\boldsymbol{I}: \mathbb{Z} \to \mathbb{R}$ and the location of an anatomical structure of interest $p_{GT} \in \mathbb{R}^3$ within $\boldsymbol{I}$, the task can be formulated as learning a navigation strategy to $p_{GT}$ in the voxel grid. This can also be interpreted as finding voxel-based navigation trajectories from any arbitrary starting point $p_0$ to a destination point $p_k$ within the image $\boldsymbol{I}$, such that $|p_k - p_{GT}|$ is minimized. In the domain of reinforcement learning, this problem can be modelled as a Markov Decision Process (MDP): $$ \mathcal{M} := \left(\mathcal{S}, \mathcal{A}, \mathcal{T}, \mathcal{R}, \gamma\right) $$ where $\mathcal{S}$ represents the finite state of states, and $s_t \in \mathcal{S}$ denotes the state of the agent at time $t$, and is defined as $s_t = \boldsymbol{I}(p_t)$. ...