Magnification-Aware Distillation (MAD): A Self-Supervised Framework for Unified Representation Learning in Gigapixel Whole-Slide Images

Whole-slide images (WSIs) contain tissue information distributed across multiple magnification levels, yet most self-supervised methods treat these scales as independent views. This separation prevents models from learning representations that remain stable when resolution changes, a key requirement for practical neuropathology workflows. This study introduces Magnification-Aware Distillation (MAD), a self-supervised strategy that links low-magnification context with spatially aligned high-magnification detail, enabling the model to learn how coarse tissue structure relates to fine cellular patterns. The resulting foundation model, MAD-NP, is trained entirely through this cross-scale correspondence without annotations. A linear classifier trained only on 10x embeddings maintains 96.7% of its performance when applied to unseen 40x tiles, demonstrating strong resolution-invariant representation learning. Segmentation outputs remain consistent across magnifications, preserving anatomical boundaries and minimizing noise. These results highlight the feasibility of scalable, magnification-robust WSI analysis using a unified embedding space

Enhancing Low Dose Computed Tomography Images Using Consistency Training Techniques

Diffusion models have significant impact on wide range of generative tasks, especially on image inpainting and restoration. Although the improvements on aiming for decreasing number of function evaluations (NFE), the iterative results are still computationally expensive. Consistency models are as a new family of generative models, enable single-step sampling of high quality data without the need for adversarial training. In this paper, we introduce the beta noise distribution, which provides flexibility in adjusting noise levels. This is combined with a sinusoidal curriculum that enhances the learning of the trajectory between the noise distribution and the posterior distribution of interest, allowing High Noise Improved Consistency Training (HN-iCT) to be trained in a supervised fashion. Additionally, High Noise Improved Consistency Training with Image Condition (HN-iCT-CN) architecture is introduced, enables to take Low Dose images as a condition for extracting significant features by Weighted Attention Gates (WAG).Our results indicate that unconditional image generation using HN-iCT significantly outperforms basic CT and iCT training techniques with NFE=1 on the CIFAR10 and CelebA datasets. Moreover, our image-conditioned model demonstrates exceptional performance in enhancing low-dose (LD) CT scans.

DINO-LG: A Task-Specific DINO Model for Coronary Calcium Scoring

Coronary artery disease (CAD), one of the most common cause of mortality in the world. Coronary artery calcium (CAC) scoring using computed tomography (CT) is key for risk assessment to prevent coronary disease. Previous studies on risk assessment and calcification detection in CT scans primarily use approaches based on UNET architecture, frequently implemented on pre-built models. However, these models are limited by the availability of annotated CT scans containing CAC and suffering from imbalanced dataset, decreasing performance of CAC segmentation and scoring. In this study, we extend this approach by incorporating the self-supervised learning (SSL) technique of DINO (self-distillation with no labels) to eliminate limitations of scarce annotated data in CT scans. The DINO model's ability to train without requiring CAC area annotations enhances its robustness in generating distinct features. The DINO model is trained on to focus specifically on calcified areas by using labels, aiming to generate features that effectively capture and highlight key characteristics. The label-guided DINO (DINO-LG) enhances classification by distinguishing CT slices that contain calcification from those that do not, performing 57% better than the standard DINO model in this task. CAC scoring and segmentation tasks are performed by a basic U-NET architecture, fed specifically with CT slices containing calcified areas as identified by the DINO-LG model. This targeted identification performed by DINO-LG model improves CAC segmentation performance by approximately 10% and significant increase in CAC scoring accuracy.

High Noise Scheduling is a Must

Consistency models possess high capabilities for image generation, advancing sampling steps to a single step through their advanced techniques. Current advancements move one step forward consistency training techniques and eliminates the limitation of distillation training. Even though the proposed curriculum and noise scheduling in improved training techniques yield better results than basic consistency models, it lacks well balanced noise distribution and its consistency between curriculum. In this study, it is investigated the balance between high and low noise levels in noise distribution and offered polynomial noise distribution to maintain the stability. This proposed polynomial noise distribution is also supported with a predefined Karras noises to prevent unique noise levels arises with Karras noise generation algorithm. Furthermore, by elimination of learned noisy steps with a curriculum based on sinusoidal function increase the performance of the model in denoising. To make a fair comparison with the latest released consistency model training techniques, experiments are conducted with same hyper-parameters except curriculum and noise distribution. The models utilized during experiments are determined with low depth to prove the robustness of our proposed technique. The results show that the polynomial noise distribution outperforms the model trained with log-normal noise distribution, yielding a 33.54 FID score after 100,000 training steps with constant discretization steps. Additionally, the implementation of a sinusoidal-based curriculum enhances denoising performance, resulting in a FID score of 30.48.