Diabetic retinopathy image classification method based on GreenBen data augmentation

For the diagnosis of diabetes retinopathy (DR) images, this paper proposes a classification method based on artificial intelligence. The core lies in a new data augmentation method, GreenBen, which first extracts the green channel grayscale image from the retinal image and then performs Ben enhancement. Considering that diabetes macular edema (DME) is a complication closely related to DR, this paper constructs a joint classification framework of DR and DME based on multi task learning and attention module, and uses GreenBen to enhance its data to reduce the difference of DR images and improve the accuracy of model classification. We conducted extensive experiments on three publicly available datasets, and our method achieved the best results. For GreenBen, whether based on the ResNet50 network or the Swin Transformer network, whether for individual classification or joint DME classification, compared with other data augmentation methods, GreenBen achieved stable and significant improvements in DR classification results, with an accuracy increase of 10%.

ODC-SA Net: Orthogonal Direction Enhancement and Scale Aware Network for Polyp Segmentation

Accurate polyp segmentation is crucial for the early detection and prevention of colorectal cancer. However, the existing polyp detection methods sometimes ignore multi-directional features and drastic changes in scale. To address these challenges, we design an Orthogonal Direction Enhancement and Scale Aware Network (ODC-SA Net) for polyp segmentation. The Orthogonal Direction Convolutional (ODC) block can extract multi-directional features using transposed rectangular convolution kernels through forming an orthogonal feature vector basis, which solves the issue of random feature direction changes and reduces computational load. Additionally, the Multi-scale Fusion Attention (MSFA) mechanism is proposed to emphasize scale changes in both spatial and channel dimensions, enhancing the segmentation accuracy for polyps of varying sizes. Extraction with Re-attention Module (ERA) is used to re-combinane effective features, and Structures of Shallow Reverse Attention Mechanism (SRA) is used to enhance polyp edge with low level information. A large number of experiments conducted on public datasets have demonstrated that the performance of this model is superior to state-of-the-art methods.

Mask-TS Net: Mask Temperature Scaling Uncertainty Calibration for Polyp Segmentation

Lots of popular calibration methods in medical images focus on classification, but there are few comparable studies on semantic segmentation. In polyp segmentation of medical images, we find most diseased area occupies only a small portion of the entire image, resulting in previous models being not well-calibrated for lesion regions but well-calibrated for background, despite their seemingly better Expected Calibration Error (ECE) scores overall. Therefore, we proposed four-branches calibration network with Mask-Loss and Mask-TS strategies to more focus on the scaling of logits within potential lesion regions, which serves to mitigate the influence of background interference. In the experiments, we compare the existing calibration methods with the proposed Mask Temperature Scaling (Mask-TS). The results indicate that the proposed calibration network outperforms other methods both qualitatively and quantitatively.