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Found 7 papers, 2 papers with code
Transfer Learning for Segmentation Problems: Choose the Right Encoder and Skip the Decoder
In this work, we investigate the impact of transfer learning for segmentation problems, being pixel-wise classification problems that can be tackled with encoder-decoder architectures.
Detecting genetic alterations in BRAF and NTRK as oncogenic drivers in digital pathology images: towards model generalization within and across multiple thyroid cohorts.
In this paper, we describe the machine learning problem of identifying different types of tumors based on digital pathology images.
Towards Fine-grained Visual Representations by Combining Contrastive Learning with Image Reconstruction and Attention-weighted Pooling
This paper presents Contrastive Reconstruction, ConRec - a self-supervised learning algorithm that obtains image representations by jointly optimizing a contrastive and a self-reconstruction loss.
Chargrid-OCR: End-to-end trainable Optical Character Recognition through Semantic Segmentation and Object Detection
We present an end-to-end trainable approach for optical character recognition (OCR) on printed documents.
Chargrid-OCR: End-to-end Trainable Optical Character Recognition for Printed Documents using Instance Segmentation
We present an end-to-end trainable approach for Optical Character Recognition (OCR) on printed documents.
Chargrid: Towards Understanding 2D Documents
We introduce a novel type of text representation that preserves the 2D layout of a document.
Multi-Target Shrinkage
For the sample mean and the sample covariance as specific instances, we derive conditions under which the optimality of MTS is applicable.
