Titlebook: Medical Image Computing and Computer Assisted Intervention – MICCAI 2022; 25th International C Linwei Wang,Qi Dou,Shuo Li Conference procee

exorbitant

ReMix: A General and?Efficient Framework for?Multiple Instance Learning Based Whole Slide Image Clasimages and slide-level labels. Yet the decent performance of deep learning comes from harnessing massive datasets and diverse samples, urging the need for efficient training pipelines for scaling to large datasets and data augmentation techniques for diversifying samples. However, current MIL-based

Repetitions

S,R: Self-supervised Spectral Regression for?Hyperspectral Histopathology Image Classificationications such as computational pathology. But, the lack of adequate annotated data and the high spatiospectral dimensions of HSIs usually make classification networks prone to overfit. Thus, learning a general representation which can be transferred to the downstream tasks is imperative. To our know

柱廊

Distilling Knowledge from?Topological Representations for?Pathological Complete Response Predictionicator for both personalized treatment and prognosis. Current prevailing approaches for pCR prediction either require complex feature engineering or employ sophisticated topological computation, which are not efficient while yielding limited performance boosts. In this paper, we present a simple yet

手勢

陰郁

Clinical-Realistic Annotation for?Histopathology Images with?Probabilistic Semi-supervision: A Worsttwo sources: localization requiring high expertise, and delineation requiring tedious and time-consuming work. Existing methods of easing the annotation effort mostly focus on the latter one, the extreme of which is replacing the delineation with a single label for all cases. We postulate that under

MIRTH

End-to-End Learning for?Image-Based Detection of?Molecular Alterations in?Digital Pathologystage identifies areas of interest (e.g. tumor tissue), while the second stage processes cropped tiles from these areas in a supervised fashion. During inference, a large number of tiles are combined into a unified prediction for the entire slide. A major drawback of such approaches is the requireme

chance

Offstage

Sample Hardness Based Gradient Loss for?Long-Tailed Cervical Cell Detectionning a detector to detect the cancer cells in a WSI (Whole Slice Image) image captured from the TCT (Thinprep Cytology Test) specimen, head categories (e.g. normal cells and inflammatory cells) typically have a much larger number of samples than tail categories (e.g. cancer cells). Most existing sta

endocardium

Test-Time Image-to-Image Translation Ensembling Improves Out-of-Distribution Generalization in?Histoiations, caused by the use of different protocols across medical centers (staining, scanner), can strongly harm algorithms generalization on unseen protocols. This motivates the development of new methods to limit such loss of generalization. In this paper, to enhance robustness on unseen target pro

Type-1-Diabetes

Predicting Molecular Traits from?Tissue Morphology Through Self-interactive Multi-instance Learningwhich relies on a fixed pretrained model for feature extraction and an instance-bag classifier. We argue that such a two-step approach is not optimal at capturing both fine-grained features at tile level and global features at slide level optimal to the task. We propose a self-interactive MIL that i